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"dt": "provider_reported" }, { "t": 1356998400, "label": "2013", "v": 339046.9, "q": "", "dt": "provider_reported" }, { "t": 1388534400, "label": "2014", "v": 266077.6, "q": "", "dt": "provider_reported" }, { "t": 1420070400, "label": "2015", "v": 397730.7, "q": "", "dt": "provider_reported" }, { "t": 1451606400, "label": "2016", "v": 301582.1, "q": "", "dt": "provider_reported" }, { "t": 1483228800, "label": "2017", "v": 322172.5, "q": "", "dt": "provider_reported" }, { "t": 1514764800, "label": "2018", "v": 283954.8, "q": "", "dt": "provider_reported" }, { "t": 1546300800, "label": "2019", "v": 431571.7, "q": "", "dt": "provider_reported" }, { "t": 1577836800, "label": "2020", "v": 331280.8, "q": "", "dt": "provider_reported" } ], "fit": { "ok": true, "model": "ols", "n": 26, "coef": { "a": 525041.9715888198, "b": -7598.955532590001 }, "k": 2, "slope_per_year": -7598.955532590001, "fitted": [ 525041.9715888198, 517448.2172578263, 509833.6581204466, 502239.9037894532, 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"validation": { "ok": true, "holdout": 6, "rmse": 122773.43302044774, "mae": 110938.61166666674 } } ], "best_model": "loglinear", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 26, "slope_per_year": -7598.955532590001, "growth_rate_per_year": null, "r2": 0.4576545711235772, "residual_sd": 64574.9369079533, "slope_se": 1950.214983804706, "slope_se_naive": 1688.563401722158, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.1430731057821484, "n": 26, "effective_n": 19.491403600488855, "se_inflation": 1.1549551422325575, "material": false }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -157, "z": -3.438481917174619, "significant_95": true, "direction": "decreasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 16, "label": "2011", "timestamp": 1293840000, "k": 143, "p_value": 0.002407719989656406, "significant_95": true, "confounded_by_trend": true, "note": "The series has a significant monotonic trend. On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 331280.8, "fitted_at_last": 335073.2844756663, "gap": -3792.484475666308, "gap_in_sd": -0.05872997570361096, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.320814933853929, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.11461159254374619, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": false, "interpretation": "Stationarity could not be rejected: the series is consistent with stationarity.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "inconclusive_weak_evidence", "interpretation": "Neither test rejected its own null hypothesis; the data does not carry enough information to separate the two cases." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 2.545335590595797, "lags": 5, "dof": 3, "p_value": 0.4695928727507937, "residuals_are_white_5pct": true, "acf": [ { "lag": 1, "r": 0.1430731057821484 }, { "lag": 2, "r": -0.04731327697063003 }, { "lag": 3, "r": -0.013912135733531325 }, { "lag": 4, "r": 0.22652038923490825 }, { "lag": 5, "r": -0.07151099897354637 } ], "interpretation": "The residuals are consistent with white noise: the model appears to have left no capturable structure in the series.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 0.2219881202916321, "skewness": 0.21279642589354517, "kurtosis": 2.8457831541427416, "excess_kurtosis": -0.15421684585725837, "p_value": 0.8949440647504908, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "approximately symmetric" }, "breusch_pagan": { "ok": true, "statistic": 0.010583923959451624, "p_value": 0.881825790296939, "homoscedastic_5pct": true, "slope_of_variance": 0.003804448721855643, "interpretation": "The residual spread looks constant over time; a single uncertainty band is defensible." }, "violations": [], "verdict": "None of the three assumptions appears to be violated on this series; the analytical intervals are defensible." }, "theilsen": { "ok": true, "model": "theilsen", "n": 26, "slope_per_year": -7960.051641586862, "growth_rate_per_year": null, "r2": 0.455649949599897, "residual_sd": 64694.16803364953, "slope_se": 2118.216586364011, "slope_se_naive": 1825.8598301830511, "sen_interval": { "lower": -11213.18928431756, "upper": -4055.8187499999985, "se_equivalent": 1825.8598301830511 }, "autocorrelation": { "ok": true, "lag1": 0.14744093263123761, "n": 26, "effective_n": 19.31823688802608, "se_inflation": 1.160120043909203, "material": false }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 94603.64225789621 }, "change_percent": -43.083398906615635 }, "segments": [ { "from": "1995", "to": "2008", "trend": { "ok": true, "model": "ols", "n": 14, "slope_per_year": -121.96299608881652, "growth_rate_per_year": null, "r2": 7.35416633915742e-5, "residual_sd": 61921.41928402906, "slope_se": 4105.393280932385, "slope_se_naive": 4105.393280932385, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": -0.12276424660764396, "n": 14, "effective_n": 14.0, "se_inflation": 1.0, "material": false }, "dof": 12, "significant_95": false, "fragile": true, "span_years": 12.999315537303216, "x0_timestamp": 788918400 } }, { "from": "2009", "to": "2020", "trend": { "ok": true, "model": "ols", "n": 12, "slope_per_year": -10022.074030742833, "growth_rate_per_year": null, "r2": 0.2699955701479283, "residual_sd": 62313.031657660504, "slope_se": 5286.925816192773, "slope_se_naive": 5211.251520792895, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.014415904997684546, "n": 12, "effective_n": 11.658935040115306, "se_inflation": 1.014521328532683, "material": false }, "dof": 10, "significant_95": false, "fragile": true, "span_years": 10.99794661190965, "x0_timestamp": 1230768000 } } ], "sha256": "751a589020c5bbc4e492fa01413482a5979a8c094c1601e53af339baab023b1b", "analysis_id": "FISH-A1", "live_url": "ai_lab.php?dataset=evidence&indicator=OECD_MARINE_LANDINGS_T&entity=TUR&from=1995&to=2022&model=ols&aggregate=raw&run=0" }, { "indicator": "OECD_SOE_FLEET_GROSS_TONNAGE_T_NA_NA", "entity": "TUR", "dataset": "evidence", "meta": { "indicator_key": "OECD_SOE_FLEET_GROSS_TONNAGE_T_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FLEET_GROSS_TONNAGE.T._Z._Z", "title_tr": "Balıkçı filosunun brüt tonajı", "title_en": "Gross tonnage of fishing vessels of all sizes", "theme": "economy", "relationship": "context", "unit": "GT (brüt tonaj)", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: Gross tonnage of fishing vessels of all sizes; birim: Tonnes; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. Bazı OECD grup toplamları sağlayıcının interpolasyon\/doldurma işlemlerini içerir; uygulama yeni tahmin üretmez. Kaynağın balıkçılık toplamı içsu faaliyetlerini de kapsayabilir; deniz ekonomisi için bağlam göstergesidir. GT geminin kapalı hacmine dayalı boyutsuz brüt tonajdır, metrik ton cinsinden yük\/kütle değildir; sağlayıcı T\/bininci ölçek kodu GT toplamına dönüştürüldü.", "source_url": "https:\/\/sdmx.oecd.org\/public\/rest\/data\/OECD.ENV.EPI,DSD_SOE@DF_SOE,1.0\/.A.AQUACUL_MARINE_SPECIES+BASE_PPP+CF_BUILT_EXP+CF_POP_EXP+COASTAL_100_POP+COASTAL_10_POP+COASTAL_LC_10KM+COASTAL_LC_1KM+COASTAL_LE1+COASTAL_LE10+FFS_FFSO_GSSE+FISH_EMP_MARINE_FISH_TOT+FLEET_GROSS_TONNAGE+FLEET_TOTAL_SHIP_COUNT+MARINE+PAT_O_CCACOAST+PAT_O_CCMFISH+PAT_O_DESA+PAT_O_OCEAN+PAT_O_POL+PAT_O_RE+PAT_O_TRA+PINE_OCEAN_TOT+STOCK_BIO_PC+STOCK_NOT_BIO_PC+THREATENED_MAR_FISH+TRADE_TOTAL_N_EXP_USD+TRADE_TOTAL_N_IMP_USD+TRANSPORT_CONT_SEA+TRANSPORT_SEA_CAB...?startPeriod=1990&dimensionAtObservation=AllDimensions&format=csvfilewithlabels" }, "institution": "OECD", "entity_name": "Türkiye", "rows": [ { "period": "1995", "value": 102800.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "1996", "value": 103200.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "1997", "value": 121750.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "1998", "value": 133200.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "1999", "value": 133100.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2000", "value": 136020.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2001", "value": 147000.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2002", "value": 180140.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2003", "value": 191390.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2004", "value": 195590.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2005", "value": 195160.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2006", "value": 189780.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2007", "value": 187330.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2008", "value": 185440.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2009", "value": 186500.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2010", "value": 185810.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2011", "value": 183500.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2012", "value": 179940.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2013", "value": 175330.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2014", "value": 169760.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2015", "value": 169360.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2016", "value": 172320.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2017", "value": 174700.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2018", "value": 170790.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2019", "value": 171780.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2020", "value": 172190.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2021", "value": 174670.0, "quality_flag": "A", "data_type": "provider_reported" }, { "period": "2022", "value": 176320.0, "quality_flag": "A", "data_type": "provider_reported" } ], "provenance": [ { "provenance_key": "5491418a29cbfb861ca08b38cced5c1073a6efb94d1ced72b180966e9e9b8da0", "source_url": "https:\/\/sdmx.oecd.org\/public\/rest\/data\/OECD.ENV.EPI,DSD_SOE@DF_SOE,1.0\/.A.AQUACUL_MARINE_SPECIES+BASE_PPP+CF_BUILT_EXP+CF_POP_EXP+COASTAL_100_POP+COASTAL_10_POP+COASTAL_LC_10KM+COASTAL_LC_1KM+COASTAL_LE1+COASTAL_LE10+FFS_FFSO_GSSE+FISH_EMP_MARINE_FISH_TOT+FLEET_GROSS_TONNAGE+FLEET_TOTAL_SHIP_COUNT+MARINE+PAT_O_CCACOAST+PAT_O_CCMFISH+PAT_O_DESA+PAT_O_OCEAN+PAT_O_POL+PAT_O_RE+PAT_O_TRA+PINE_OCEAN_TOT+STOCK_BIO_PC+STOCK_NOT_BIO_PC+THREATENED_MAR_FISH+TRADE_TOTAL_N_EXP_USD+TRADE_TOTAL_N_IMP_USD+TRANSPORT_CONT_SEA+TRANSPORT_SEA_CAB...?startPeriod=1990&dimensionAtObservation=AllDimensions&format=csvfilewithlabels", "retrieved_at": "2026-08-31 08:26:26" } ], "run": { "meta": { "indicator_key": "OECD_SOE_FLEET_GROSS_TONNAGE_T_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FLEET_GROSS_TONNAGE.T._Z._Z", "title_tr": "Balıkçı filosunun brüt tonajı", "title_en": "Gross tonnage of fishing vessels of all sizes", "theme": "economy", "relationship": "context", "unit": "GT (brüt tonaj)", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: Gross tonnage of fishing vessels of all sizes; birim: Tonnes; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. Bazı OECD grup toplamları sağlayıcının interpolasyon\/doldurma işlemlerini içerir; uygulama yeni tahmin üretmez. Kaynağın balıkçılık toplamı içsu faaliyetlerini de kapsayabilir; deniz ekonomisi için bağlam göstergesidir. 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-5.0260342282532795, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": true, "interpretation": "The unit root is rejected at 5%: the series may be treated as stationary under the stated deterministic terms.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.2253545106290611, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "conflicting", "interpretation": "The tests conflict (ADF says stationary, KPSS says not). This usually signals a break, changing variance or long memory; a single differencing decision cannot be drawn safely from this data." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 47.77675283635156, "lags": 6, "dof": 4, "p_value": 1.0505252768398807e-9, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.8609878868291436 }, { "lag": 2, "r": 0.6455871763347434 }, { "lag": 3, "r": 0.4611892901993244 }, { "lag": 4, "r": 0.3052215242445479 }, { "lag": 5, "r": 0.12357086401119219 }, { "lag": 6, "r": -0.08623467176755685 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 1.079409074945357, "skewness": 0.16961649771162043, "kurtosis": 2.099928473208508, "excess_kurtosis": -0.9000715267914918, "p_value": 0.5829204580843427, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "thin-tailed" }, "breusch_pagan": { "ok": true, "statistic": 5.049285891182782, "p_value": 0.023330150918559337, "homoscedastic_5pct": false, "slope_of_variance": -0.07434594499875587, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "otokorelasyon", "changing variance" ], "verdict": "Assumption(s) violated: otokorelasyon, changing variance. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 28, "slope_per_year": 896.6403631775803, "growth_rate_per_year": null, "r2": 0.24066263552573064, "residual_sd": 23612.403424928387, "slope_se": 2368.8300689588154, "slope_se_naive": 775.3816499636595, "sen_interval": { "lower": -764.4767441860473, "upper": 2275.0193236714977, "se_equivalent": 775.3816499636595 }, "autocorrelation": { "ok": true, "lag1": 0.8422170470676532, "n": 28, "effective_n": 3.0, "se_inflation": 3.0550504633038935, "material": true }, "dof": 26, "significant_95": false, "fragile": false, "span_years": 27.000684462696782, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 2229.9588934925832 }, "change_percent": 71.51750972762645 }, "common_run": { "meta": { "indicator_key": "OECD_SOE_FLEET_GROSS_TONNAGE_T_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FLEET_GROSS_TONNAGE.T._Z._Z", "title_tr": "Balıkçı filosunun brüt tonajı", "title_en": "Gross tonnage of fishing vessels of all sizes", "theme": "economy", "relationship": "context", "unit": "GT (brüt tonaj)", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: Gross tonnage of fishing vessels of all sizes; birim: Tonnes; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. Bazı OECD grup toplamları sağlayıcının interpolasyon\/doldurma işlemlerini içerir; uygulama yeni tahmin üretmez. Kaynağın balıkçılık toplamı içsu faaliyetlerini de kapsayabilir; deniz ekonomisi için bağlam göstergesidir. GT geminin kapalı hacmine dayalı boyutsuz brüt tonajdır, metrik ton cinsinden yük\/kütle değildir; sağlayıcı T\/bininci ölçek kodu GT toplamına dönüştürüldü.", "source_url": "https:\/\/sdmx.oecd.org\/public\/rest\/data\/OECD.ENV.EPI,DSD_SOE@DF_SOE,1.0\/.A.AQUACUL_MARINE_SPECIES+BASE_PPP+CF_BUILT_EXP+CF_POP_EXP+COASTAL_100_POP+COASTAL_10_POP+COASTAL_LC_10KM+COASTAL_LC_1KM+COASTAL_LE1+COASTAL_LE10+FFS_FFSO_GSSE+FISH_EMP_MARINE_FISH_TOT+FLEET_GROSS_TONNAGE+FLEET_TOTAL_SHIP_COUNT+MARINE+PAT_O_CCACOAST+PAT_O_CCMFISH+PAT_O_DESA+PAT_O_OCEAN+PAT_O_POL+PAT_O_RE+PAT_O_TRA+PINE_OCEAN_TOT+STOCK_BIO_PC+STOCK_NOT_BIO_PC+THREATENED_MAR_FISH+TRADE_TOTAL_N_EXP_USD+TRADE_TOTAL_N_IMP_USD+TRANSPORT_CONT_SEA+TRANSPORT_SEA_CAB...?startPeriod=1990&dimensionAtObservation=AllDimensions&format=csvfilewithlabels", "data_types": [ "provider_reported" ] }, "preprocessing": { "aggregate": "raw", "deseasonalise": false, "applied": [], "raw_points": 26, "used_points": 26 }, "quality": { "ok": true, "n": 26, "by_class": { "validated": 0, "provisional": 0, "modelled": 0, "unflagged": 0, "unclassified": 26 }, "class_share": { "validated": 0.0, "provisional": 0.0, "modelled": 0.0, "unflagged": 0.0, "unclassified": 1.0 }, "by_flag": [ { "flag": "A", "n": 26, "share": 1, "classes": [ "unclassified" ] } ], "distinct_flags": 1, "caveated": 0, "caveated_share": 0, "mixed": false, "filter_applied": false }, "describe": { "n": 26, "first_label": "1995", "last_label": "2020", "first_value": 102800.0, "last_value": 172190.0, "min": 102800.0, "max": 195590.0, "mean": 165918.46153846153, "median": 173510.0, "sd": 27509.1934730639, "span_seconds": 788918400 }, "regularity": { "ok": true, "mean_gap_seconds": 31556736, "median_gap_seconds": 31536000, "max_gap_seconds": 31622400, "cv": 0.0011693218981525912, "regular": true }, "eligibility": { "projectable": true, "semantics": "ratio", "blocking": [], "warnings": [] }, "points": [ { "t": 788918400, "label": "1995", "v": 102800.0, "q": "A", "dt": "provider_reported" }, { "t": 820454400, "label": "1996", "v": 103200.0, "q": "A", "dt": "provider_reported" }, { "t": 852076800, "label": "1997", "v": 121750.0, "q": "A", "dt": "provider_reported" }, { "t": 883612800, "label": "1998", "v": 133200.0, "q": "A", "dt": "provider_reported" }, { "t": 915148800, "label": "1999", "v": 133100.0, "q": "A", "dt": "provider_reported" }, { "t": 946684800, "label": "2000", "v": 136020.0, "q": "A", "dt": "provider_reported" }, { "t": 978307200, "label": "2001", "v": 147000.0, "q": "A", "dt": "provider_reported" }, { "t": 1009843200, "label": "2002", "v": 180140.0, "q": "A", "dt": "provider_reported" }, { "t": 1041379200, "label": "2003", "v": 191390.0, "q": "A", "dt": "provider_reported" }, { "t": 1072915200, "label": "2004", "v": 195590.0, "q": "A", "dt": "provider_reported" }, { "t": 1104537600, "label": "2005", "v": 195160.0, "q": "A", "dt": "provider_reported" }, { "t": 1136073600, "label": "2006", "v": 189780.0, "q": "A", "dt": "provider_reported" }, { "t": 1167609600, "label": "2007", "v": 187330.0, "q": "A", "dt": "provider_reported" }, { "t": 1199145600, "label": "2008", "v": 185440.0, "q": "A", "dt": "provider_reported" }, { "t": 1230768000, "label": "2009", "v": 186500.0, "q": "A", "dt": "provider_reported" }, { "t": 1262304000, "label": "2010", "v": 185810.0, "q": "A", "dt": "provider_reported" }, { "t": 1293840000, "label": "2011", "v": 183500.0, "q": "A", "dt": "provider_reported" }, { "t": 1325376000, "label": "2012", "v": 179940.0, "q": "A", "dt": "provider_reported" }, { "t": 1356998400, "label": "2013", "v": 175330.0, "q": "A", "dt": "provider_reported" }, { "t": 1388534400, "label": "2014", "v": 169760.0, "q": "A", "dt": "provider_reported" }, { "t": 1420070400, "label": "2015", "v": 169360.0, "q": "A", "dt": "provider_reported" }, { "t": 1451606400, "label": "2016", "v": 172320.0, "q": "A", "dt": "provider_reported" }, { "t": 1483228800, "label": "2017", "v": 174700.0, "q": "A", "dt": "provider_reported" }, { "t": 1514764800, "label": "2018", "v": 170790.0, "q": "A", "dt": "provider_reported" }, { "t": 1546300800, "label": "2019", "v": 171780.0, "q": "A", "dt": "provider_reported" }, { "t": 1577836800, "label": "2020", "v": 172190.0, "q": "A", "dt": "provider_reported" } ], "fit": { "ok": true, "model": "ols", "n": 26, "coef": { "a": 139019.54298508534, "b": 2151.863633272431 }, "k": 2, "slope_per_year": 2151.863633272431, "fitted": [ 139019.54298508534, 141169.93374797225, 143326.21599240127, 145476.60675528817, 147626.99751817505, 149777.38828106195, 151933.670525491, 154084.06128837788, 156234.4520512648, 158384.84281415166, 160541.1250585807, 162691.51582146762, 164841.90658435452, 166992.2973472414, 169148.57959167045, 171298.97035455733, 173449.36111744423, 175599.75188033114, 177756.0341247602, 179906.42488764707, 182056.81565053394, 184207.20641342085, 186363.4886578499, 188513.8794207368, 190664.27018362368, 192814.66094651056 ], "residuals": [ -36219.54298508534, -37969.933747972245, -21576.215992401267, -12276.606755288172, -14526.997518175049, -13757.388281061954, -4933.670525491005, 26055.93871162212, 35155.54794873521, 37205.15718584834, 34618.874941419286, 27088.48417853238, 22488.093415645475, 18447.7026527586, 17351.420408329548, 14511.029645442672, 10050.638882555766, 4340.24811966886, -2426.03412476019, -10146.424887647066, -12696.815650533943, -11887.206413420849, -11663.488657849899, -17723.879420736805, -18884.27018362368, -20624.660946510558 ], "rss": 12146803017.509233, "r2": 0.35795382274160914, "residual_sd": 22497.039636276993, "dof": 24, "aic": 523.018118006877, "x_last": 24.999315537303218, "x_mean": 12.500289580371717, "sxx": 1462.4911171387648 }, "models": [ { "key": "ols", "name": "Linear trend (OLS)", "ok": true, "r2": 0.35795382274160914, "aic": 523.018118006877, "k": 2, "residual_sd": 22497.039636276993, "slope_per_year": 2151.863633272431, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8541688357503322, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 45152.881268469624, "mae": 44660.89414797266 } }, { "key": "theilsen", "name": "Linear — Theil–Sen (robust)", "ok": true, "r2": 0.28520568643207833, "aic": 525.8088175985423, "k": 2, "residual_sd": 23737.38018608649, "slope_per_year": 1325.9327712926936, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8432476134651263, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 38080.51017625448, "mae": 37565.76704075521 } }, { "key": "quadratic", "name": "Quadratic (curvature)", "ok": true, "r2": 0.8389526055832672, "aic": 489.06111819117103, "k": 3, "residual_sd": 11509.614969569222, "slope_per_year": -7164.600819882893, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.7204311571909886, "n": 26, "effective_n": 4.224981559217003, "se_inflation": 2.4807001055240794, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 43861.79614220456, "mae": 38861.17434958525 } }, { "key": "loglinear", "name": "Log-linear (exponential)", "ok": true, "r2": 0.29455195925022426, "aic": 525.4666131846457, "k": 2, "residual_sd": 23581.680829337693, "slope_per_year": null, "growth_rate_per_year": 0.015440837111302619, "autocorrelation": { "ok": true, "lag1": 0.8601333458933719, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 60064.73069196985, "mae": 59114.25947649059 } }, { "key": "holt", "name": "Holt exponential smoothing", "ok": true, "r2": 0.9017953660472203, "aic": 474.2003405537176, "k": 2, "residual_sd": 8798.491763398928, "slope_per_year": 100.3790406005871, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.2898635814858747, "n": 26, "effective_n": 14.314340792611524, "se_inflation": 1.3477241099354054, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 19625.7019590408, "mae": 17769.847052687936 } }, { "key": "mean", "name": "Mean (reference)", "ok": true, "r2": 0.0, "aic": 532.5385893246885, "k": 1, "residual_sd": 27509.1934730639, "slope_per_year": null, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8411162630483124, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 7888.2492142849205, "mae": 7719.666666666667 } } ], "best_model": "mean", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 26, "slope_per_year": 2151.863633272431, "growth_rate_per_year": null, "r2": 0.35795382274160914, "residual_sd": 22497.039636276993, "slope_se": 1731.8281780180125, "slope_se_naive": 588.2727819162823, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.8541688357503322, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "dof": 24, "significant_95": false, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": 35, "z": 0.7494127255380579, "significant_95": false, "direction": "increasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 6, "label": "2001", "timestamp": 978307200, "k": 133, "p_value": 0.005965411671648348, "significant_95": true }, "fit_gap": { "last_observed": 172190.0, "fitted_at_last": 192814.66094651056, "gap": -20624.660946510558, "gap_in_sd": -0.9167722189213205, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -4.536869452596858, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": true, "interpretation": "The unit root is rejected at 5%: the series may be treated as stationary under the stated deterministic terms.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.2231762823174571, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "conflicting", "interpretation": "The tests conflict (ADF says stationary, KPSS says not). This usually signals a break, changing variance or long memory; a single differencing decision cannot be drawn safely from this data." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 42.45610992928044, "lags": 5, "dof": 3, "p_value": 2.0638456458859412e-8, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.8541688357503322 }, { "lag": 2, "r": 0.631037594711944 }, { "lag": 3, "r": 0.43958948973568907 }, { "lag": 4, "r": 0.2839338744588939 }, { "lag": 5, "r": 0.10002943110429871 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 1.4025899655225804, "skewness": 0.20432451756657324, "kurtosis": 1.938065729059515, "excess_kurtosis": -1.061934270940485, "p_value": 0.49594265058571074, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "thin-tailed" }, "breusch_pagan": { "ok": true, "statistic": 2.823157612685982, "p_value": 0.08879382676018026, "homoscedastic_5pct": true, "slope_of_variance": -0.06213494836623683, "interpretation": "The residual spread looks constant over time; a single uncertainty band is defensible." }, "violations": [ "otokorelasyon" ], "verdict": "Assumption(s) violated: otokorelasyon. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 26, "slope_per_year": 1325.9327712926936, "growth_rate_per_year": null, "r2": 0.28520568643207833, "residual_sd": 23737.38018608649, "slope_se": 2802.999385488453, "slope_se_naive": 952.1315492729971, "sen_interval": { "lower": -908.124315443593, "upper": 2824.2313577065556, "se_equivalent": 952.1315492729971 }, "autocorrelation": { "ok": true, "lag1": 0.8432476134651263, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "dof": 24, "significant_95": false, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 2650.892931322073 }, "change_percent": 67.5 }, "segments": [ { "from": "1995", "to": "2008", "trend": { "ok": true, "model": "ols", "n": 14, "slope_per_year": 7818.786941132607, "growth_rate_per_year": null, "r2": 0.8575311571849082, "residual_sd": 13876.178728476169, "slope_se": 1800.0797034359089, "slope_se_naive": 919.9913628529517, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.5857830991999676, "n": 14, "effective_n": 3.656891421106761, "se_inflation": 1.956626742509568, "material": true }, "dof": 12, "significant_95": true, "fragile": true, "span_years": 12.999315537303216, "x0_timestamp": 788918400 } }, { "from": "2009", "to": "2020", "trend": { "ok": true, "model": "ols", "n": 12, "slope_per_year": -1427.6665342650672, "growth_rate_per_year": null, "r2": 0.6677377379754603, "residual_sd": 3808.03712328731, "slope_se": 636.9338394251333, "slope_se_naive": 318.46691971256666, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.6320415996139649, "n": 12, "effective_n": 3.0, "se_inflation": 2.0, "material": true }, "dof": 10, "significant_95": true, "fragile": true, "span_years": 10.99794661190965, "x0_timestamp": 1230768000 } } ], "sha256": "926fe99a1d3418d46c45bd6fe75f9ef9e423de9d678b53bfa750152a7eb54096", "analysis_id": "FISH-A2", "live_url": "ai_lab.php?dataset=evidence&indicator=OECD_SOE_FLEET_GROSS_TONNAGE_T_NA_NA&entity=TUR&from=1995&to=2022&model=ols&aggregate=raw&run=0" }, { "indicator": "OECD_SOE_FLEET_TOTAL_SHIP_COUNT_VS_NA_NA", "entity": "TUR", "dataset": "evidence", "meta": { "indicator_key": "OECD_SOE_FLEET_TOTAL_SHIP_COUNT_VS_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FLEET_TOTAL_SHIP_COUNT.VS._Z._Z", "title_tr": "Toplam balıkçı teknesi sayısı", "title_en": "Total number of fishing vessels, all sizes", "theme": "economy", "relationship": "context", "unit": "gemi", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: Total number of fishing vessels, all sizes; birim: Vessels; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. Bazı OECD grup toplamları sağlayıcının interpolasyon\/doldurma işlemlerini içerir; uygulama yeni tahmin üretmez. 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"critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": true, "interpretation": "The unit root is rejected at 5%: the series may be treated as stationary under the stated deterministic terms.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.22724347107507697, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "conflicting", "interpretation": "The tests conflict (ADF says stationary, KPSS says not). This usually signals a break, changing variance or long memory; a single differencing decision cannot be drawn safely from this data." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 45.146737459821004, "lags": 6, "dof": 4, "p_value": 3.7062283351188858e-9, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.8369849375902353 }, { "lag": 2, "r": 0.610833056477789 }, { "lag": 3, "r": 0.44918728855103107 }, { "lag": 4, "r": 0.3160110970850121 }, { "lag": 5, "r": 0.12633444564459104 }, { "lag": 6, "r": -0.10734161334271877 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 0.37363976911104807, "skewness": -0.14454033596855623, "kurtosis": 2.513486871348338, "excess_kurtosis": -0.4865131286516622, "p_value": 0.8295931454676885, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "approximately symmetric" }, "breusch_pagan": { "ok": true, "statistic": 6.768025275097445, "p_value": 0.009071324792759348, "homoscedastic_5pct": false, "slope_of_variance": -0.08607425876267429, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "otokorelasyon", "changing variance" ], "verdict": "Assumption(s) violated: otokorelasyon, changing variance. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 28, "slope_per_year": -45.707682368943026, "growth_rate_per_year": null, "r2": -0.09832523274054572, "residual_sd": 2127.460451652688, "slope_se": 181.68430989990784, "slope_se_naive": 59.47015019301017, "sen_interval": { "lower": -186.46696685878965, "upper": 46.65602189781022, "se_equivalent": 59.47015019301017 }, "autocorrelation": { "ok": true, "lag1": 0.8154259660998101, "n": 28, "effective_n": 3.0, "se_inflation": 3.0550504633038935, "material": true }, "dof": 26, "significant_95": false, "fragile": false, "span_years": 27.000684462696782, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 349.9780945525972 }, "change_percent": 31.79930795847752 }, "common_run": { "meta": { "indicator_key": "OECD_SOE_FLEET_TOTAL_SHIP_COUNT_VS_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FLEET_TOTAL_SHIP_COUNT.VS._Z._Z", "title_tr": "Toplam balıkçı teknesi sayısı", "title_en": "Total number of fishing vessels, all sizes", "theme": "economy", "relationship": "context", "unit": "gemi", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: Total number of fishing vessels, all sizes; birim: Vessels; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. Bazı OECD grup toplamları sağlayıcının interpolasyon\/doldurma işlemlerini içerir; uygulama yeni tahmin üretmez. Kaynağın balıkçılık toplamı içsu faaliyetlerini de kapsayabilir; deniz ekonomisi için bağlam göstergesidir.", "source_url": "https:\/\/sdmx.oecd.org\/public\/rest\/data\/OECD.ENV.EPI,DSD_SOE@DF_SOE,1.0\/.A.AQUACUL_MARINE_SPECIES+BASE_PPP+CF_BUILT_EXP+CF_POP_EXP+COASTAL_100_POP+COASTAL_10_POP+COASTAL_LC_10KM+COASTAL_LC_1KM+COASTAL_LE1+COASTAL_LE10+FFS_FFSO_GSSE+FISH_EMP_MARINE_FISH_TOT+FLEET_GROSS_TONNAGE+FLEET_TOTAL_SHIP_COUNT+MARINE+PAT_O_CCACOAST+PAT_O_CCMFISH+PAT_O_DESA+PAT_O_OCEAN+PAT_O_POL+PAT_O_RE+PAT_O_TRA+PINE_OCEAN_TOT+STOCK_BIO_PC+STOCK_NOT_BIO_PC+THREATENED_MAR_FISH+TRADE_TOTAL_N_EXP_USD+TRADE_TOTAL_N_IMP_USD+TRANSPORT_CONT_SEA+TRANSPORT_SEA_CAB...?startPeriod=1990&dimensionAtObservation=AllDimensions&format=csvfilewithlabels", "data_types": [ "provider_reported" ] }, "preprocessing": { "aggregate": "raw", "deseasonalise": false, "applied": [], "raw_points": 26, "used_points": 26 }, "quality": { "ok": true, "n": 26, "by_class": { "validated": 0, "provisional": 0, "modelled": 0, "unflagged": 0, "unclassified": 26 }, "class_share": { "validated": 0.0, "provisional": 0.0, "modelled": 0.0, "unflagged": 0.0, "unclassified": 1.0 }, "by_flag": [ { "flag": "A", "n": 26, "share": 1, "classes": [ "unclassified" ] } ], "distinct_flags": 1, "caveated": 0, "caveated_share": 0, "mixed": false, "filter_applied": false }, "describe": { "n": 26, "first_label": "1995", "last_label": "2020", "first_value": 11560.0, "last_value": 15302.0, "min": 11560.0, "max": 18999.0, "mean": 16232.846153846154, "median": 16004.5, "sd": 2053.259315182721, "span_seconds": 788918400 }, "regularity": { "ok": true, "mean_gap_seconds": 31556736, "median_gap_seconds": 31536000, "max_gap_seconds": 31622400, "cv": 0.0011693218981525912, "regular": true }, "eligibility": { "projectable": true, "semantics": "ratio", "blocking": [], "warnings": [] }, "points": [ { "t": 788918400, "label": "1995", "v": 11560.0, "q": "A", "dt": "provider_reported" }, { "t": 820454400, "label": "1996", "v": 11575.0, "q": "A", "dt": "provider_reported" }, { "t": 852076800, "label": "1997", "v": 13620.0, "q": "A", "dt": "provider_reported" }, { "t": 883612800, "label": "1998", "v": 14875.0, "q": "A", "dt": "provider_reported" }, { "t": 915148800, "label": "1999", "v": 14818.0, "q": "A", "dt": "provider_reported" }, { "t": 946684800, "label": "2000", "v": 14975.0, "q": "A", "dt": "provider_reported" }, { "t": 978307200, "label": "2001", "v": 16132.0, "q": "A", "dt": "provider_reported" }, { "t": 1009843200, "label": "2002", "v": 18696.0, "q": "A", "dt": "provider_reported" }, { "t": 1041379200, "label": "2003", "v": 18779.0, "q": "A", "dt": "provider_reported" }, { "t": 1072915200, "label": "2004", "v": 18999.0, "q": "A", "dt": "provider_reported" }, { "t": 1104537600, "label": "2005", "v": 18836.0, "q": "A", "dt": "provider_reported" }, { "t": 1136073600, "label": "2006", "v": 18790.0, "q": "A", "dt": "provider_reported" }, { "t": 1167609600, "label": "2007", "v": 18563.0, "q": "A", "dt": "provider_reported" }, { "t": 1199145600, "label": "2008", "v": 17732.0, "q": "A", "dt": "provider_reported" }, { "t": 1230768000, "label": "2009", "v": 17469.0, "q": "A", "dt": "provider_reported" }, { "t": 1262304000, "label": "2010", "v": 17440.0, "q": "A", "dt": "provider_reported" }, { "t": 1293840000, "label": "2011", "v": 17165.0, "q": "A", "dt": "provider_reported" }, { "t": 1325376000, "label": "2012", "v": 16998.0, "q": "A", "dt": "provider_reported" }, { "t": 1356998400, "label": "2013", "v": 16437.0, "q": "A", "dt": "provider_reported" }, { "t": 1388534400, "label": "2014", "v": 15877.0, "q": "A", "dt": "provider_reported" }, { "t": 1420070400, "label": "2015", "v": 15680.0, "q": "A", "dt": "provider_reported" }, { "t": 1451606400, "label": "2016", "v": 15663.0, "q": "A", "dt": "provider_reported" }, { "t": 1483228800, "label": "2017", "v": 15406.0, "q": "A", "dt": "provider_reported" }, { "t": 1514764800, "label": "2018", "v": 15352.0, "q": "A", "dt": "provider_reported" }, { "t": 1546300800, "label": "2019", "v": 15315.0, "q": "A", "dt": "provider_reported" }, { "t": 1577836800, "label": "2020", "v": 15302.0, "q": "A", "dt": "provider_reported" } ], "fit": { "ok": true, "model": "ols", "n": 26, "coef": { "a": 15454.295114700719, "b": 62.2826402652252 }, "k": 2, "slope_per_year": 62.2826402652252, "fitted": [ 15454.295114700719, 15516.535124822025, 15578.945655519006, 15641.185665640312, 15703.42567576162, 15765.665685882926, 15828.076216579908, 15890.316226701214, 15952.55623682252, 16014.796246943826, 16077.206777640808, 16139.446787762115, 16201.686797883422, 16263.926808004728, 16326.337338701709, 16388.577348823015, 16450.81735894432, 16513.057369065627, 16575.46789976261, 16637.707909883917, 16699.947920005223, 16762.18793012653, 16824.598460823512, 16886.83847094482, 16949.078481066124, 17011.31849118743 ], "residuals": [ -3894.295114700719, -3941.535124822025, -1958.9456555190063, -766.1856656403124, -885.4256757616204, -790.6656858829265, 303.92378342009215, 2805.683773298786, 2826.44376317748, 2984.203753056174, 2758.7932223591924, 2650.5532122378845, 2361.3132021165784, 1468.0731919952723, 1142.662661298291, 1051.4226511769848, 714.1826410556787, 484.9426309343726, -138.4678997626106, -760.7079098839167, -1019.9479200052228, -1099.187930126529, -1418.5984608235121, -1534.8384709448183, -1634.0784810661244, -1709.3184911874305 ], "rss": 99723656.19769384, "r2": 0.05382693539089234, "residual_sd": 2038.4190789851277, "dof": 24, "aic": 398.1552405066625, "x_last": 24.999315537303218, "x_mean": 12.500289580371717, "sxx": 1462.4911171387648 }, "models": [ { "key": "ols", "name": "Linear trend (OLS)", "ok": true, "r2": 0.05382693539089234, "aic": 398.1552405066625, "k": 2, "residual_sd": 2038.4190789851277, "slope_per_year": 62.2826402652252, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8337744803046493, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 4049.2261656749856, "mae": 4012.625976235542 } }, { "key": "theilsen", "name": "Linear — Theil–Sen (robust)", "ok": true, "r2": -0.08629154628961944, "aic": 401.74582561943447, "k": 2, "residual_sd": 2184.1445898690504, "slope_per_year": -37.02534246575338, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8145317600051667, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 3271.6847244511287, "mae": 3242.502524953287 } }, { "key": "quadratic", "name": "Quadratic (curvature)", "ok": true, "r2": 0.77627057269544, "aic": 362.6635502196776, "k": 3, "residual_sd": 1012.5382624570029, "slope_per_year": -789.9282346995933, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.6969749105316901, "n": 26, "effective_n": 4.642762999782682, "se_inflation": 2.3664559056390355, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 4368.040386992833, "mae": 3884.462413011261 } }, { "key": "loglinear", "name": "Log-linear (exponential)", "ok": true, "r2": 0.03789641020725165, "aic": 398.58935290446004, "k": 2, "residual_sd": 2055.5076755042955, "slope_per_year": null, "growth_rate_per_year": 0.0049296569614043495, "autocorrelation": { "ok": true, "lag1": 0.8389957440600113, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 4612.381449676474, "mae": 4560.9181592889545 } }, { "key": "holt", "name": "Holt exponential smoothing", "ok": true, "r2": 0.859325412740525, "aic": 348.59986022792924, "k": 2, "residual_sd": 785.9881711082837, "slope_per_year": -102.59720481517373, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.2740340901747649, "n": 26, "effective_n": 14.815234381104299, "se_inflation": 1.3247453712076194, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 972.0999688775569, "mae": 839.6538992094938 } }, { "key": "mean", "name": "Mean (reference)", "ok": true, "r2": 0.0, "aic": 397.5938148672876, "k": 1, "residual_sd": 2053.259315182721, "slope_per_year": null, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.819077157383252, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 1026.0463472312863, "mae": 1013.7999999999993 } } ], "best_model": "holt", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 26, "slope_per_year": 62.2826402652252, "growth_rate_per_year": null, "r2": 0.05382693539089234, "residual_sd": 2038.4190789851277, "slope_se": 156.9180504044387, "slope_se_naive": 53.30241141470702, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.8337744803046493, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "dof": 24, "significant_95": false, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -19, "z": -0.3967479135201483, "significant_95": false, "direction": "decreasing" }, "agreement": "disagree", "change_point": { "ok": true, "index": 5, "label": "2000", "timestamp": 946684800, "k": 120, "p_value": 0.017587257759369722, "significant_95": true }, "fit_gap": { "last_observed": 15302.0, "fitted_at_last": 17011.31849118743, "gap": -1709.3184911874305, "gap_in_sd": -0.8385510657791001, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -5.149147197913882, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": true, "interpretation": "The unit root is rejected at 5%: the series may be treated as stationary under the stated deterministic terms.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.22541344221127615, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "conflicting", "interpretation": "The tests conflict (ADF says stationary, KPSS says not). This usually signals a break, changing variance or long memory; a single differencing decision cannot be drawn safely from this data." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 40.38039940045842, "lags": 5, "dof": 3, "p_value": 4.697932275644398e-8, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.8337744803046493 }, { "lag": 2, "r": 0.6016536313477606 }, { "lag": 3, "r": 0.4337455471422929 }, { "lag": 4, "r": 0.2939642127871671 }, { "lag": 5, "r": 0.09933167912478255 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 0.6186248113092138, "skewness": -0.09092719204639141, "kurtosis": 2.2665374785658194, "excess_kurtosis": -0.7334625214341806, "p_value": 0.7339514436255009, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "thin-tailed" }, "breusch_pagan": { "ok": true, "statistic": 4.240652931559858, "p_value": 0.037236982936119226, "homoscedastic_5pct": false, "slope_of_variance": -0.07615260152589877, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "otokorelasyon", "changing variance" ], "verdict": "Assumption(s) violated: otokorelasyon, changing variance. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 26, "slope_per_year": -37.02534246575338, "growth_rate_per_year": null, "r2": -0.08629154628961944, "residual_sd": 2184.1445898690504, "slope_se": 226.9556045428073, "slope_se_naive": 77.09298563826708, "sen_interval": { "lower": -198.89036076662907, "upper": 103.31414293537789, "se_equivalent": 77.09298563826708 }, "autocorrelation": { "ok": true, "lag1": 0.8145317600051667, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "dof": 24, "significant_95": false, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 452.5004604049223 }, "change_percent": 32.370242214532865 }, "segments": [ { "from": "1995", "to": "2008", "trend": { "ok": true, "model": "ols", "n": 14, "slope_per_year": 585.1741336577065, "growth_rate_per_year": null, "r2": 0.803466540874687, "residual_sd": 1260.1289347391594, "slope_se": 149.55930764396868, "slope_se_naive": 83.5466131365136, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.5243266267000989, "n": 14, "effective_n": 4.368766581618478, "se_inflation": 1.7901301085609729, "material": true }, "dof": 12, "significant_95": true, "fragile": true, "span_years": 12.999315537303216, "x0_timestamp": 788918400 } }, { "from": "2009", "to": "2020", "trend": { "ok": true, "model": "ols", "n": 12, "slope_per_year": -231.253060781063, "growth_rate_per_year": null, "r2": 0.9138447723962873, "residual_sd": 268.49050822055256, "slope_se": 44.419149373341824, "slope_se_naive": 22.45391585133707, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.5929514246752764, "n": 12, "effective_n": 3.0663727896739896, "se_inflation": 1.9782362091063412, "material": true }, "dof": 10, "significant_95": true, "fragile": true, "span_years": 10.99794661190965, "x0_timestamp": 1230768000 } } ], "sha256": "575861529d999286a02ddd27102c2a9e3bf4428ccef6ebf3d559048336b81aaa", "analysis_id": "FISH-A3", "live_url": "ai_lab.php?dataset=evidence&indicator=OECD_SOE_FLEET_TOTAL_SHIP_COUNT_VS_NA_NA&entity=TUR&from=1995&to=2022&model=ols&aggregate=raw&run=0" }, { "indicator": "OECD_SOE_FISH_EMP_MARINE_FISH_TOT_PS_NA_NA", "entity": "TUR", "dataset": "evidence", "meta": { "indicator_key": "OECD_SOE_FISH_EMP_MARINE_FISH_TOT_PS_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FISH_EMP_MARINE_FISH_TOT.PS._Z._Z", "title_tr": "İçsu balıkçılığı dışındaki balıkçılık istihdamı", "title_en": "People employed in fishing sectors excluding inland fisheries", "theme": "economy", "relationship": "direct", "unit": "kişi", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: People employed in fishing sectors excluding inland fisheries; birim: Persons; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. 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On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 41600.0, "fitted_at_last": 36961.040415762975, "gap": 4638.959584237025, "gap_in_sd": 0.9566258203114492, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.9702857296401586, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.1283981124179973, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": false, "interpretation": "Stationarity could not be rejected: the series is consistent with stationarity.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "inconclusive_weak_evidence", "interpretation": "Neither test rejected its own null hypothesis; the data does not carry enough information to separate the two cases." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 33.84643979217133, "lags": 6, "dof": 4, "p_value": 8.012259674540159e-7, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.7533724883340784 }, { "lag": 2, "r": 0.46866341954871255 }, { "lag": 3, "r": 0.25719691737737044 }, { "lag": 4, "r": 0.10671976676836346 }, { "lag": 5, "r": -0.14285648205567936 }, { "lag": 6, "r": -0.38725073608593297 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 2.4300547889292856, "skewness": 0.34848790384008005, "kurtosis": 1.7362243758067184, "excess_kurtosis": -1.2637756241932816, "p_value": 0.29670188619338256, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "thin-tailed" }, "breusch_pagan": { "ok": true, "statistic": 0.6270170856672603, "p_value": 0.43418212197325756, "homoscedastic_5pct": true, "slope_of_variance": 0.026198835804422393, "interpretation": "The residual spread looks constant over time; a single uncertainty band is defensible." }, "violations": [ "otokorelasyon" ], "verdict": "Assumption(s) violated: otokorelasyon. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 28, "slope_per_year": -726.5000000000002, "growth_rate_per_year": null, "r2": 0.6189623584344446, "residual_sd": 4870.070735762825, "slope_se": 338.9201430478578, "slope_se_naive": 126.1392029958487, "sen_interval": { "lower": -900.6164383561634, "upper": -406.15076261243644, "se_equivalent": 126.1392029958487 }, "autocorrelation": { "ok": true, "lag1": 0.7566700336700449, "n": 28, "effective_n": 3.878496773241179, "se_inflation": 2.6868739852352785, "material": true }, "dof": 26, "significant_95": true, "fragile": false, "span_years": 27.000684462696782, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 3443.292997504956 }, "change_percent": -21.760391198044005 }, "common_run": { "meta": { "indicator_key": "OECD_SOE_FISH_EMP_MARINE_FISH_TOT_PS_NA_NA", "source_key": "oecd_sustainable_ocean", "provider_code": "FISH_EMP_MARINE_FISH_TOT.PS._Z._Z", "title_tr": "İçsu balıkçılığı dışındaki balıkçılık istihdamı", "title_en": "People employed in fishing sectors excluding inland fisheries", "theme": "economy", "relationship": "direct", "unit": "kişi", "frequency": "annual", "description_tr": "OECD Sustainable Ocean Economy seçimi. Ölçü: People employed in fishing sectors excluding inland fisheries; birim: Persons; koruma kategorisi: Not applicable; dönüş süresi: Not applicable. OECD'nin yıllık boyutunda seyrek gözlenen yıllar aynen korunur.", "caveat_tr": "Ülke kapsamı ve son gözlem yılı değişir; boş yıllar sıfır değildir. Sağlayıcı tahmin\/provizyon\/revizyon işaretleri ham dosyada ve gözlem metadatasında korunur. Bazı OECD grup toplamları sağlayıcının interpolasyon\/doldurma işlemlerini içerir; uygulama yeni tahmin üretmez.", "source_url": "https:\/\/sdmx.oecd.org\/public\/rest\/data\/OECD.ENV.EPI,DSD_SOE@DF_SOE,1.0\/.A.AQUACUL_MARINE_SPECIES+BASE_PPP+CF_BUILT_EXP+CF_POP_EXP+COASTAL_100_POP+COASTAL_10_POP+COASTAL_LC_10KM+COASTAL_LC_1KM+COASTAL_LE1+COASTAL_LE10+FFS_FFSO_GSSE+FISH_EMP_MARINE_FISH_TOT+FLEET_GROSS_TONNAGE+FLEET_TOTAL_SHIP_COUNT+MARINE+PAT_O_CCACOAST+PAT_O_CCMFISH+PAT_O_DESA+PAT_O_OCEAN+PAT_O_POL+PAT_O_RE+PAT_O_TRA+PINE_OCEAN_TOT+STOCK_BIO_PC+STOCK_NOT_BIO_PC+THREATENED_MAR_FISH+TRADE_TOTAL_N_EXP_USD+TRADE_TOTAL_N_IMP_USD+TRANSPORT_CONT_SEA+TRANSPORT_SEA_CAB...?startPeriod=1990&dimensionAtObservation=AllDimensions&format=csvfilewithlabels", "data_types": [ "provider_reported" ] }, "preprocessing": { "aggregate": "raw", "deseasonalise": false, "applied": [], "raw_points": 26, "used_points": 26 }, "quality": { "ok": true, "n": 26, "by_class": { "validated": 0, "provisional": 0, "modelled": 0, "unflagged": 0, "unclassified": 26 }, "class_share": { "validated": 0.0, "provisional": 0.0, "modelled": 0.0, "unflagged": 0.0, "unclassified": 1.0 }, "by_flag": [ { "flag": "A", "n": 26, "share": 1, "classes": [ "unclassified" ] } ], "distinct_flags": 1, "caveated": 0, "caveated_share": 0, "mixed": false, "filter_applied": false }, "describe": { "n": 26, "first_label": "1995", "last_label": "2020", "first_value": 53170.0, "last_value": 41370.0, "min": 34180.0, "max": 56000.0, "mean": 47402.692307692305, "median": 50635.0, "sd": 7885.210743001005, "span_seconds": 788918400 }, "regularity": { "ok": true, "mean_gap_seconds": 31556736, "median_gap_seconds": 31536000, "max_gap_seconds": 31622400, "cv": 0.0011693218981525912, "regular": true }, "eligibility": { "projectable": true, "semantics": "ratio", "blocking": [], "warnings": [] }, "points": [ { "t": 788918400, "label": "1995", "v": 53170.0, "q": "A", "dt": "provider_reported" }, { "t": 820454400, "label": "1996", "v": 52610.0, "q": "A", "dt": "provider_reported" }, { "t": 852076800, "label": "1997", "v": 54000.0, "q": "A", "dt": "provider_reported" }, { "t": 883612800, "label": "1998", "v": 54000.0, "q": "A", "dt": "provider_reported" }, { "t": 915148800, "label": "1999", "v": 50000.0, "q": "A", "dt": "provider_reported" }, { "t": 946684800, "label": "2000", "v": 50000.0, "q": "A", "dt": "provider_reported" }, { "t": 978307200, "label": "2001", "v": 53000.0, "q": "A", "dt": "provider_reported" }, { "t": 1009843200, "label": "2002", "v": 56000.0, "q": "A", "dt": "provider_reported" }, { "t": 1041379200, "label": "2003", "v": 51270.0, "q": "A", "dt": "provider_reported" }, { "t": 1072915200, "label": "2004", "v": 49840.0, "q": "A", "dt": "provider_reported" }, { "t": 1104537600, "label": "2005", "v": 55630.0, "q": "A", "dt": "provider_reported" }, { "t": 1136073600, "label": "2006", "v": 54010.0, "q": "A", "dt": "provider_reported" }, { "t": 1167609600, "label": "2007", "v": 54620.0, "q": "A", "dt": "provider_reported" }, { "t": 1199145600, "label": "2008", "v": 53890.0, "q": "A", "dt": "provider_reported" }, { "t": 1230768000, "label": "2009", "v": 54910.0, "q": "A", "dt": "provider_reported" }, { "t": 1262304000, "label": "2010", "v": 54170.0, "q": "A", "dt": "provider_reported" }, { "t": 1293840000, "label": "2011", "v": 45200.0, "q": "A", "dt": "provider_reported" }, { "t": 1325376000, "label": "2012", "v": 41050.0, "q": "A", "dt": "provider_reported" }, { "t": 1356998400, "label": "2013", "v": 37500.0, "q": "A", "dt": "provider_reported" }, { "t": 1388534400, "label": "2014", "v": 36720.0, "q": "A", "dt": "provider_reported" }, { "t": 1420070400, "label": "2015", "v": 35820.0, "q": "A", "dt": "provider_reported" }, { "t": 1451606400, "label": "2016", "v": 36910.0, "q": "A", "dt": "provider_reported" }, { "t": 1483228800, "label": "2017", "v": 36660.0, "q": "A", "dt": "provider_reported" }, { "t": 1514764800, "label": "2018", "v": 35940.0, "q": "A", "dt": "provider_reported" }, { "t": 1546300800, "label": "2019", "v": 34180.0, "q": "A", "dt": "provider_reported" }, { "t": 1577836800, "label": "2020", "v": 41370.0, "q": "A", "dt": "provider_reported" } ], "fit": { "ok": true, "model": "ols", "n": 26, "coef": { "a": 57692.4497855136, "b": -823.1615285119908 }, "k": 2, "slope_per_year": -823.1615285119908, "fitted": [ 57692.4497855136, 56869.8516803613, 56044.99988177023, 55222.40177661793, 54399.803671465634, 53577.205566313336, 52752.35376772227, 51929.75566256997, 51107.157557417675, 50284.55945226538, 49459.707653674304, 48637.10954852201, 47814.51144336971, 46991.91333821741, 46167.061539626346, 45344.46343447405, 44521.86532932175, 43699.26722416945, 42874.41542557838, 42051.81732042608, 41229.219215273784, 40406.62111012149, 39581.769311530414, 38759.171206378116, 37936.57310122582, 37113.97499607352 ], "residuals": [ -4522.4497855136, -4259.851680361302, -2044.999881770229, -1222.4017766179313, -4399.803671465634, -3577.205566313336, 247.64623227772972, 4070.2443374300274, 162.84244258232502, -444.55945226537733, 6170.292346325696, 5372.890451477993, 6805.488556630291, 6898.086661782589, 8742.938460373654, 8825.536565525952, 678.1346706782497, -2649.2672241694527, -5374.41542557838, -5331.817320426082, -5409.219215273784, -3496.6211101214867, -2921.7693115304137, -2819.171206378116, -3756.5731012258184, 4256.025003926479 ], "rss": 563437186.3124871, "r2": 0.6375243076343992, "residual_sd": 4845.260512055772, "dof": 24, "aic": 443.1782167916386, "x_last": 24.999315537303218, "x_mean": 12.500289580371717, "sxx": 1462.4911171387648 }, "models": [ { "key": "ols", "name": "Linear trend (OLS)", "ok": true, "r2": 0.6375243076343992, "aic": 443.1782167916386, "k": 2, "residual_sd": 4845.260512055772, "slope_per_year": -823.1615285119908, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.7304958602282933, "n": 26, "effective_n": 4.04919063668836, "se_inflation": 2.5339763779965936, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 6790.090065958127, "mae": 6194.7288953059215 } }, { "key": "theilsen", "name": "Linear — Theil–Sen (robust)", "ok": true, "r2": 0.6246373209085352, "aic": 444.0865342361773, "k": 2, "residual_sd": 4930.639305994306, "slope_per_year": -780.5342465753444, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.7401450968185921, "n": 26, "effective_n": 3.882565594713125, "se_inflation": 2.58777952974982, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 10111.82011793459, "mae": 9788.925156896747 } }, { "key": "quadratic", "name": "Quadratic (curvature)", "ok": true, "r2": 0.7419577404470131, "aic": 436.3425315364149, "k": 3, "residual_sd": 4176.041533222213, "slope_per_year": -2067.4880756096754, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.547835105171544, "n": 26, "effective_n": 7.595309879108167, "se_inflation": 1.8501797586328503, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 11560.698525491207, "mae": 9140.02344973799 } }, { "key": "loglinear", "name": "Log-linear (exponential)", "ok": true, "r2": 0.5971956225049587, "aic": 445.92105070223647, "k": 2, "residual_sd": 5107.692921164794, "slope_per_year": null, "growth_rate_per_year": -0.018368377232540145, "autocorrelation": { "ok": true, "lag1": 0.7446349026636672, "n": 26, "effective_n": 3.805663019011963, "se_inflation": 2.613794916313041, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 6260.095182410577, "mae": 5725.753167387576 } }, { "key": "holt", "name": "Holt exponential smoothing", "ok": true, "r2": 0.8199385008446456, "aic": 424.98708377984667, "k": 2, "residual_sd": 3414.979643712231, "slope_per_year": -499.02241648274685, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.14128003303896294, "n": 26, "effective_n": 19.562875450940915, "se_inflation": 1.1528434303208122, "material": false }, "validation": { "ok": true, "holdout": 6, "rmse": 4715.670669083918, "mae": 3416.885602798207 } }, { "key": "mean", "name": "Mean (reference)", "ok": true, "r2": 0.0, "aic": 467.56296121609, "k": 1, "residual_sd": 7885.210743001005, "slope_per_year": null, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8915259135988721, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 13943.481162058013, "mae": 13766.166666666666 } } ], "best_model": "holt", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 26, "slope_per_year": -823.1615285119908, "growth_rate_per_year": null, "r2": 0.6375243076343992, "residual_sd": 4845.260512055772, "slope_se": 321.0502969915931, "slope_se_naive": 126.698220148927, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.7304958602282933, "n": 26, "effective_n": 4.04919063668836, "se_inflation": 2.5339763779965936, "material": true }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -151, "z": -3.3078400510317296, "significant_95": true, "direction": "decreasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 15, "label": "2010", "timestamp": 1262304000, "k": 160, "p_value": 0.0004428103748661776, "significant_95": true, "confounded_by_trend": true, "note": "The series has a significant monotonic trend. On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 41370.0, "fitted_at_last": 37113.97499607352, "gap": 4256.025003926479, "gap_in_sd": 0.8783893029769645, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.7698057537153193, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.1633518467121643, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "non_stationary", "interpretation": "Both tests show non-stationarity: differencing (ARIMA d≥1) is required." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 27.151312418268663, "lags": 5, "dof": 3, "p_value": 1.0457249202944992e-5, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.7304958602282933 }, { "lag": 2, "r": 0.4860167634998055 }, { "lag": 3, "r": 0.3201159653301848 }, { "lag": 4, "r": 0.15511149136817415 }, { "lag": 5, "r": -0.10840415931910737 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 2.849061388987282, "skewness": 0.6084990451691383, "kurtosis": 1.9281705038968966, "excess_kurtosis": -1.0718294961031034, "p_value": 0.24062136164430847, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "thin-tailed" }, "breusch_pagan": { "ok": true, "statistic": 0.22005610329424208, "p_value": 0.6440129214172663, "homoscedastic_5pct": true, "slope_of_variance": 0.017347427794981038, "interpretation": "The residual spread looks constant over time; a single uncertainty band is defensible." }, "violations": [ "otokorelasyon" ], "verdict": "Assumption(s) violated: otokorelasyon. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 26, "slope_per_year": -780.5342465753444, "growth_rate_per_year": null, "r2": 0.6246373209085352, "residual_sd": 4930.639305994306, "slope_se": 377.65065424080177, "slope_se_naive": 145.93617806278579, "sen_interval": { "lower": -1003.5698180061203, "upper": -431.5, "se_equivalent": 145.93617806278579 }, "autocorrelation": { "ok": true, "lag1": 0.7401450968185921, "n": 26, "effective_n": 3.882565594713125, "se_inflation": 2.58777952974982, "material": true }, "dof": 24, "significant_95": false, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 2218.565602666131 }, "change_percent": -22.192965958247136 }, "segments": [ { "from": "1995", "to": "2008", "trend": { "ok": true, "model": "ols", "n": 14, "slope_per_year": 107.70451783552785, "growth_rate_per_year": null, "r2": 0.049315306922667945, "residual_sd": 2059.002212459716, "slope_se": 140.9495929097689, "slope_se_naive": 136.51195250683242, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.03197924630019367, "n": 14, "effective_n": 13.1323285815915, "se_inflation": 1.0325073396244508, "material": false }, "dof": 12, "significant_95": false, "fragile": true, "span_years": 12.999315537303216, "x0_timestamp": 788918400 } }, { "from": "2009", "to": "2020", "trend": { "ok": true, "model": "ols", "n": 12, "slope_per_year": -1462.6646416247038, "growth_rate_per_year": null, "r2": 0.5565743881387866, "residual_sd": 4936.627831485867, "slope_se": 681.5095664035509, "slope_se_naive": 412.8511903538015, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.46307849735565454, "n": 12, "effective_n": 4.403767838415526, "se_inflation": 1.6507390128135928, "material": true }, "dof": 10, "significant_95": false, "fragile": true, "span_years": 10.99794661190965, "x0_timestamp": 1230768000 } } ], "sha256": "b70945e6ec60bd9bdd4ff0359cd4b1d203205a27f9a32398a81db7b13d2a261b", "analysis_id": "FISH-A4", "live_url": "ai_lab.php?dataset=evidence&indicator=OECD_SOE_FISH_EMP_MARINE_FISH_TOT_PS_NA_NA&entity=TUR&from=1995&to=2022&model=ols&aggregate=raw&run=0" }, { "indicator": "wb_wealth_NW_NCA_FISH_TO", "entity": "TUR", "dataset": "evidence", "meta": { "indicator_key": "wb_wealth_NW_NCA_FISH_TO", "source_key": "wb_wealth", "provider_code": "NW.NCA.FISH.TO", "title_tr": "Deniz balık stokları doğal sermayesi", "title_en": "Renewable natural capital, fisheries (real chained 2019 US$)", "theme": "economy", "relationship": "direct", "unit": "real chained 2019 US$", "frequency": "annual", "description_tr": "Wealth Accounts kapsamında deniz balık stoklarının doğal sermaye değeri.", "caveat_tr": "Sağlayıcının model temelli servet hesabıdır; yıllık balık satış geliri veya stok tonajı değildir. Zincirlenmiş varlık kategorileri basitçe toplanamaz. Ayrıntılı seri metaverisindeki 2019 birimi esas alınmıştır.", "source_url": "https:\/\/api.worldbank.org\/v2\/sources\/59\/series\/NW.NCA.FISH.TO\/metadata?format=json" }, "institution": "World Bank", "entity_name": "Türkiye", "rows": [ { "period": "1995", "value": 582879551.964441, "quality_flag": "", "data_type": "provider_reported" }, { "period": "1996", "value": 569342523.585784, "quality_flag": "", "data_type": "provider_reported" }, { "period": "1997", "value": 556399435.868718, "quality_flag": "", "data_type": "provider_reported" }, { "period": "1998", "value": 547888360.138181, "quality_flag": "", "data_type": "provider_reported" }, { "period": "1999", "value": 539578335.831675, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2000", "value": 531235349.465136, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2001", "value": 521312518.386592, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2002", "value": 510722664.059413, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2003", "value": 501714343.724122, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2004", "value": 493007886.599205, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2005", "value": 485322270.371943, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2006", "value": 479208863.773529, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2007", "value": 477237698.375756, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2008", "value": 473591349.714734, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2009", "value": 469755623.280895, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2010", "value": 464574386.497535, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2011", "value": 457800302.697022, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2012", "value": 452600435.597408, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2013", "value": 446597831.309049, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2014", "value": 442564916.003464, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2015", "value": 439272341.199102, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2016", "value": 432438112.82825, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2017", "value": 423890008.963395, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2018", "value": 414770903.418831, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2019", "value": 405651797.874267, "quality_flag": "", "data_type": "provider_reported" }, { "period": "2020", "value": 396532692.329704, "quality_flag": "", "data_type": "provider_reported" } ], "provenance": [ { "provenance_key": "c7233cfa1f5560cb28d8a3173ae8781230c2a16556477575e8a84fd2ebc73e6f", "source_url": "https:\/\/api.worldbank.org\/v2\/country\/all\/indicator\/NW.NCA.FISH.TO?format=json&source=59&per_page=20000", "retrieved_at": "2026-08-31 08:25:05" } ], "run": { "meta": { "indicator_key": "wb_wealth_NW_NCA_FISH_TO", "source_key": "wb_wealth", "provider_code": "NW.NCA.FISH.TO", "title_tr": "Deniz balık stokları doğal sermayesi", "title_en": "Renewable natural capital, fisheries (real chained 2019 US$)", "theme": "economy", "relationship": "direct", "unit": "real chained 2019 US$", "frequency": "annual", "description_tr": "Wealth Accounts kapsamında deniz balık stoklarının doğal sermaye değeri.", "caveat_tr": "Sağlayıcının model temelli servet hesabıdır; yıllık balık satış geliri veya stok tonajı değildir. Zincirlenmiş varlık kategorileri basitçe toplanamaz. Ayrıntılı seri metaverisindeki 2019 birimi esas alınmıştır.", "source_url": "https:\/\/api.worldbank.org\/v2\/sources\/59\/series\/NW.NCA.FISH.TO\/metadata?format=json", "data_types": [ "provider_reported" ] }, "preprocessing": { "aggregate": "raw", "deseasonalise": false, "applied": [], "raw_points": 26, "used_points": 26 }, "quality": { "ok": true, "n": 26, "by_class": { "validated": 0, "provisional": 0, "modelled": 0, "unflagged": 26, "unclassified": 0 }, "class_share": { "validated": 0.0, "provisional": 0.0, "modelled": 0.0, "unflagged": 1.0, "unclassified": 0.0 }, "by_flag": [ { "flag": "—", "n": 26, "share": 1, "classes": [ "unflagged" ] } ], "distinct_flags": 1, "caveated": 0, "caveated_share": 0, "mixed": false, "filter_applied": false }, "describe": { "n": 26, "first_label": "1995", "last_label": "2020", "first_value": 582879551.964441, "last_value": 396532692.329704, "min": 396532692.329704, "max": 582879551.964441, "mean": 481380403.9945444, "median": 475414524.04524505, 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"2012", "v": 452600435.597408, "q": "", "dt": "provider_reported" }, { "t": 1356998400, "label": "2013", "v": 446597831.309049, "q": "", "dt": "provider_reported" }, { "t": 1388534400, "label": "2014", "v": 442564916.003464, "q": "", "dt": "provider_reported" }, { "t": 1420070400, "label": "2015", "v": 439272341.199102, "q": "", "dt": "provider_reported" }, { "t": 1451606400, "label": "2016", "v": 432438112.82825, "q": "", "dt": "provider_reported" }, { "t": 1483228800, "label": "2017", "v": 423890008.963395, "q": "", "dt": "provider_reported" }, { "t": 1514764800, "label": "2018", "v": 414770903.418831, "q": "", "dt": "provider_reported" }, { "t": 1546300800, "label": "2019", "v": 405651797.874267, "q": "", "dt": "provider_reported" }, { "t": 1577836800, "label": "2020", "v": 396532692.329704, "q": "", "dt": "provider_reported" } ], "fit": { "ok": true, "model": "ols", "n": 26, "coef": { "a": 564749679.6755097, "b": -6669387.54857919 }, "k": 2, "slope_per_year": -6669387.54857919, "fitted": [ 564749679.6755097, 558084857.0739179, 551401774.6843765, 544736952.0827848, 538072129.481193, 531407306.8796011, 524724224.49005973, 518059401.88846797, 511394579.2868762, 504729756.6852844, 498046674.295743, 491381851.6941512, 484717029.0925594, 478052206.49096763, 471369124.10142624, 464704301.4998344, 458039478.89824265, 451374656.2966508, 444691573.9071095, 438026751.3055177, 431361928.7039259, 424697106.1023341, 418014023.71279275, 411349201.1112009, 404684378.5096091, 398019555.90801734 ], "residuals": [ 18129872.28893125, 11257666.511866093, 4997661.18434155, 3151408.055396199, 1506206.3504821062, -171957.41446512938, -3411706.1034677625, -7336737.829054952, -9680235.562754214, -11721870.086079359, -12724403.923799992, -12172987.92062223, -7479330.716803372, -4460856.7762336135, -1613500.820531249, -129915.00229942799, -239176.2012206316, 1225779.3007571697, 1906257.4019395113, 4538164.69794631, 7910412.495176077, 7741006.725915909, 5875985.250602245, 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"autocorrelation": { "ok": true, "lag1": 0.7827314714534382, "n": 26, "effective_n": 3.1687227339993407, "se_inflation": 2.8644718200801846, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 8095233.153857057, "mae": 7507445.87598892 } }, { "key": "quadratic", "name": "Quadratic (curvature)", "ok": true, "r2": 0.9890475645973693, "aic": 811.0277723865141, "k": 3, "residual_sd": 5624285.502288569, "slope_per_year": -4183514.9507122496, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.7394627145532671, "n": 26, "effective_n": 3.894288371311692, "se_inflation": 2.5838816641396654, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 24774932.00276991, "mae": 21113917.32330638 } }, { "key": "loglinear", "name": "Log-linear (exponential)", "ok": true, "r2": 0.9864441940355331, "aic": 814.5723529158724, "k": 2, "residual_sd": 6125379.168887714, "slope_per_year": null, "growth_rate_per_year": -0.013724578525098008, "autocorrelation": { "ok": true, "lag1": 0.7691909137141134, "n": 26, "effective_n": 3.3919664615702234, "se_inflation": 2.7686043648487555, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 5992357.519529337, "mae": 5186248.871516029 } }, { "key": "holt", "name": "Holt exponential smoothing", "ok": true, "r2": 0.9981051822599588, "aic": 763.4123534927716, "k": 2, "residual_sd": 2290097.78353314, "slope_per_year": -8916603.792700393, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.5811063942117748, "n": 26, "effective_n": 6.888362345737925, "se_inflation": 1.942802626607257, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 8852923.030658474, "mae": 6816911.228652407 } }, { "key": "mean", "name": "Mean (reference)", "ok": true, "r2": 0.0, "aic": 924.396801729019, "k": 1, "residual_sd": 51547299.95387072, "slope_per_year": null, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8563022167317832, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 82743545.57630324, "mae": 81407422.9266387 } } ], "best_model": "loglinear", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 26, "slope_per_year": -6669387.54857919, "growth_rate_per_year": null, "r2": 0.9792944501432989, "residual_sd": 7570310.443438525, "slope_se": 582764.539435221, "slope_se_naive": 197955.271294906, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.8011282968158882, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -325, "z": -7.14146244336267, "significant_95": true, "direction": "decreasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 12, "label": "2007", "timestamp": 1167609600, "k": 169, "p_value": 0.00016729669445945505, "significant_95": true, "confounded_by_trend": true, "note": "The series has a significant monotonic trend. On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 396532692.329704, "fitted_at_last": 398019555.90801734, "gap": -1486863.5783133507, "gap_in_sd": -0.1964072133398534, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.4866039730059186, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.19321775785069548, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "non_stationary", "interpretation": "Both tests show non-stationarity: differencing (ARIMA d≥1) is required." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 35.370726128962666, "lags": 5, "dof": 3, "p_value": 3.51510812329597e-7, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.8011282968158882 }, { "lag": 2, "r": 0.5854987347005397 }, { "lag": 3, "r": 0.3955262721883071 }, { "lag": 4, "r": 0.20014475702273948 }, { "lag": 5, "r": 0.012635740877274254 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 0.100595950133004, "skewness": 0.1518719325174854, "kurtosis": 2.9755569114801266, "excess_kurtosis": -0.024443088519873424, "p_value": 0.950946024074917, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "approximately symmetric" }, "breusch_pagan": { "ok": true, "statistic": 4.739997724364812, "p_value": 0.027842490671025018, "homoscedastic_5pct": false, "slope_of_variance": -0.08051141248704446, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "otokorelasyon", "changing variance" ], "verdict": "Assumption(s) violated: otokorelasyon, changing variance. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 26, "slope_per_year": -6568069.09746049, "growth_rate_per_year": null, "r2": 0.9789331068611452, "residual_sd": 7636081.439235594, "slope_se": 727697.0973669332, "slope_se_naive": 254042.3306892797, "sen_interval": { "lower": -7025545.578145791, "upper": -6029699.641843814, "se_equivalent": 254042.3306892797 }, "autocorrelation": { "ok": true, "lag1": 0.7827314714534382, "n": 26, "effective_n": 3.1687227339993407, "se_inflation": 2.8644718200801846, "material": true }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 28036275.270539895 }, "change_percent": -31.97004578505187 }, "common_run": { "meta": { "indicator_key": "wb_wealth_NW_NCA_FISH_TO", "source_key": "wb_wealth", "provider_code": "NW.NCA.FISH.TO", "title_tr": "Deniz balık stokları doğal sermayesi", "title_en": "Renewable natural capital, fisheries (real chained 2019 US$)", "theme": "economy", "relationship": "direct", "unit": "real chained 2019 US$", "frequency": "annual", "description_tr": "Wealth Accounts kapsamında deniz balık stoklarının doğal sermaye değeri.", "caveat_tr": "Sağlayıcının model temelli servet hesabıdır; yıllık balık satış geliri veya stok tonajı değildir. Zincirlenmiş varlık kategorileri basitçe toplanamaz. Ayrıntılı seri metaverisindeki 2019 birimi esas alınmıştır.", "source_url": "https:\/\/api.worldbank.org\/v2\/sources\/59\/series\/NW.NCA.FISH.TO\/metadata?format=json", "data_types": [ "provider_reported" ] }, "preprocessing": { "aggregate": "raw", "deseasonalise": false, "applied": [], "raw_points": 26, "used_points": 26 }, "quality": { "ok": true, "n": 26, "by_class": { "validated": 0, "provisional": 0, "modelled": 0, "unflagged": 26, "unclassified": 0 }, "class_share": { "validated": 0.0, "provisional": 0.0, "modelled": 0.0, "unflagged": 1.0, "unclassified": 0.0 }, "by_flag": [ { "flag": "—", "n": 26, "share": 1, "classes": [ "unflagged" ] } ], "distinct_flags": 1, "caveated": 0, "caveated_share": 0, "mixed": false, "filter_applied": false }, "describe": { "n": 26, "first_label": "1995", "last_label": "2020", "first_value": 582879551.964441, "last_value": 396532692.329704, "min": 396532692.329704, "max": 582879551.964441, "mean": 481380403.9945444, "median": 475414524.04524505, 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"2012", "v": 452600435.597408, "q": "", "dt": "provider_reported" }, { "t": 1356998400, "label": "2013", "v": 446597831.309049, "q": "", "dt": "provider_reported" }, { "t": 1388534400, "label": "2014", "v": 442564916.003464, "q": "", "dt": "provider_reported" }, { "t": 1420070400, "label": "2015", "v": 439272341.199102, "q": "", "dt": "provider_reported" }, { "t": 1451606400, "label": "2016", "v": 432438112.82825, "q": "", "dt": "provider_reported" }, { "t": 1483228800, "label": "2017", "v": 423890008.963395, "q": "", "dt": "provider_reported" }, { "t": 1514764800, "label": "2018", "v": 414770903.418831, "q": "", "dt": "provider_reported" }, { "t": 1546300800, "label": "2019", "v": 405651797.874267, "q": "", "dt": "provider_reported" }, { "t": 1577836800, "label": "2020", "v": 396532692.329704, "q": "", "dt": "provider_reported" } ], "fit": { "ok": true, "model": "ols", "n": 26, "coef": { "a": 564749679.6755097, "b": -6669387.54857919 }, "k": 2, "slope_per_year": -6669387.54857919, 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3421702.307630062, 967419.3646578789, -1486863.5783133507 ], "rss": 1375430405040825.5, "r2": 0.9792944501432989, "residual_sd": 7570310.443438525, "dof": 24, "aic": 825.5856105827369, "x_last": 24.999315537303218, "x_mean": 12.500289580371717, "sxx": 1462.4911171387648 }, "models": [ { "key": "ols", "name": "Linear trend (OLS)", "ok": true, "r2": 0.9792944501432989, "aic": 825.5856105827369, "k": 2, "residual_sd": 7570310.443438525, "slope_per_year": -6669387.54857919, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.8011282968158882, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 11167437.327270564, "mae": 10819343.701760253 } }, { "key": "theilsen", "name": "Linear — Theil–Sen (robust)", "ok": true, "r2": 0.9789331068611452, "aic": 826.0354363053978, "k": 2, "residual_sd": 7636081.439235594, "slope_per_year": -6568069.09746049, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.7827314714534382, "n": 26, "effective_n": 3.1687227339993407, "se_inflation": 2.8644718200801846, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 8095233.153857057, "mae": 7507445.87598892 } }, { "key": "quadratic", "name": "Quadratic (curvature)", "ok": true, "r2": 0.9890475645973693, "aic": 811.0277723865141, "k": 3, "residual_sd": 5624285.502288569, "slope_per_year": -4183514.9507122496, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.7394627145532671, "n": 26, "effective_n": 3.894288371311692, "se_inflation": 2.5838816641396654, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 24774932.00276991, "mae": 21113917.32330638 } }, { "key": "loglinear", "name": "Log-linear (exponential)", "ok": true, "r2": 0.9864441940355331, "aic": 814.5723529158724, "k": 2, "residual_sd": 6125379.168887714, "slope_per_year": null, "growth_rate_per_year": -0.013724578525098008, 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26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 82743545.57630324, "mae": 81407422.9266387 } } ], "best_model": "loglinear", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 26, "slope_per_year": -6669387.54857919, "growth_rate_per_year": null, "r2": 0.9792944501432989, "residual_sd": 7570310.443438525, "slope_se": 582764.539435221, "slope_se_naive": 197955.271294906, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.8011282968158882, "n": 26, "effective_n": 3.0, "se_inflation": 2.943920288775949, "material": true }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -325, "z": -7.14146244336267, "significant_95": true, "direction": "decreasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 12, "label": "2007", "timestamp": 1167609600, "k": 169, "p_value": 0.00016729669445945505, "significant_95": true, "confounded_by_trend": true, "note": "The series has a significant monotonic trend. On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 396532692.329704, "fitted_at_last": 398019555.90801734, "gap": -1486863.5783133507, "gap_in_sd": -0.1964072133398534, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.4866039730059186, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.19321775785069548, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "non_stationary", "interpretation": "Both tests show non-stationarity: differencing (ARIMA d≥1) is required." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 35.370726128962666, "lags": 5, "dof": 3, "p_value": 3.51510812329597e-7, "residuals_are_white_5pct": false, "acf": [ { "lag": 1, "r": 0.8011282968158882 }, { "lag": 2, "r": 0.5854987347005397 }, { "lag": 3, "r": 0.3955262721883071 }, { "lag": 4, "r": 0.20014475702273948 }, { "lag": 5, "r": 0.012635740877274254 } ], "interpretation": "Autocorrelation remains in the residuals: the model has missed part of the structure in the data. The confidence intervals are too narrow and the forecast can be improved.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 0.100595950133004, "skewness": 0.1518719325174854, "kurtosis": 2.9755569114801266, "excess_kurtosis": -0.024443088519873424, "p_value": 0.950946024074917, "normal_5pct": true, "interpretation": "The residuals are consistent with normality; the analytical intervals are defensible on that assumption.", "shape": "approximately symmetric" }, "breusch_pagan": { "ok": true, "statistic": 4.739997724364812, "p_value": 0.027842490671025018, "homoscedastic_5pct": false, "slope_of_variance": -0.08051141248704446, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "otokorelasyon", "changing variance" ], "verdict": "Assumption(s) violated: otokorelasyon, changing variance. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 26, "slope_per_year": -6568069.09746049, "growth_rate_per_year": null, "r2": 0.9789331068611452, "residual_sd": 7636081.439235594, "slope_se": 727697.0973669332, "slope_se_naive": 254042.3306892797, "sen_interval": { "lower": -7025545.578145791, "upper": -6029699.641843814, "se_equivalent": 254042.3306892797 }, "autocorrelation": { "ok": true, "lag1": 0.7827314714534382, "n": 26, "effective_n": 3.1687227339993407, "se_inflation": 2.8644718200801846, "material": true }, "dof": 24, "significant_95": true, "fragile": false, "span_years": 24.999315537303218, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 28036275.270539895 }, "change_percent": -31.97004578505187 }, "segments": [ { "from": "1995", "to": "2008", "trend": { "ok": true, "model": "ols", "n": 14, "slope_per_year": -8568492.111015892, "growth_rate_per_year": null, "r2": 0.9829412204454703, "residual_sd": 4914848.300617121, "slope_se": 566794.229350195, "slope_se_naive": 325854.69492556807, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.5031722241318254, "n": 14, "effective_n": 4.627273409187511, "se_inflation": 1.7394078961472765, "material": true }, "dof": 12, "significant_95": true, "fragile": true, "span_years": 12.999315537303216, "x0_timestamp": 788918400 } }, { "from": "2009", "to": "2020", "trend": { "ok": true, "model": "ols", "n": 12, "slope_per_year": -6386062.095021851, "growth_rate_per_year": null, "r2": 0.9809522090206282, "residual_sd": 3364870.5947524193, "slope_se": 562809.6254571859, "slope_se_naive": 281404.81272859295, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.6337067805185901, "n": 12, "effective_n": 3.0, "se_inflation": 2.0, "material": true }, "dof": 10, "significant_95": true, "fragile": true, "span_years": 10.99794661190965, "x0_timestamp": 1230768000 } } ], "sha256": 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"slope_se": 0.01836713649238853, "slope_se_naive": 0.013052502052222591, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.3288900444287058, "n": 28, "effective_n": 14.140431584070289, "se_inflation": 1.4071736145991225, "material": true }, "dof": 26, "significant_95": true, "fragile": false, "span_years": 27.000684462696782, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -266, "z": -5.235477665470097, "significant_95": true, "direction": "decreasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 13, "label": "2008", "timestamp": 1199145600, "k": 172, "p_value": 0.0008136088277369472, "significant_95": true, "confounded_by_trend": true, "note": "The series has a significant monotonic trend. On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 1.7113594600725954, "fitted_at_last": 1.3743066008481848, "gap": 0.33705285922441064, "gap_in_sd": 0.6041316004849961, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.0466192079418075, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.17935368377546815, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "non_stationary", "interpretation": "Both tests show non-stationarity: differencing (ARIMA d≥1) is required." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 4.752136655215783, "lags": 6, "dof": 4, "p_value": 0.3136879704401128, "residuals_are_white_5pct": true, "acf": [ { "lag": 1, "r": 0.3288900444287058 }, { "lag": 2, "r": -0.0216678296480982 }, { "lag": 3, "r": 0.03878174725913676 }, { "lag": 4, "r": 0.17813242346312924 }, { "lag": 5, "r": 0.019523590980197267 }, { "lag": 6, "r": -0.07177042120072138 } ], "interpretation": "The residuals are consistent with white noise: the model appears to have left no capturable structure in the series.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 11.397251297054199, "skewness": 1.1071604546136766, "kurtosis": 5.205868412052432, "excess_kurtosis": 2.205868412052432, "p_value": 0.0033505671514741657, "normal_5pct": false, "interpretation": "The residuals are not normally distributed. Analytical confidence intervals rest on that assumption; the block bootstrap interval is more reliable on this series.", "shape": "right-skewed" }, "breusch_pagan": { "ok": true, "statistic": 7.708438036629815, "p_value": 0.005534002279554217, "homoscedastic_5pct": false, "slope_of_variance": -0.09185980059483703, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "non-normal distribution", "changing variance" ], "verdict": "Assumption(s) violated: non-normal distribution, changing variance. 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0.014828511226461671, -1.749939852928943, 8.584561722396813, 2.7055002214276485, 1.0024477305421158, 2.164841983642077 ], "rss": 619.3614743717219, "r2": 0.556001341249994, "residual_sd": 4.880737154174884, "dof": 26, "aic": 90.70156759956924, "x_last": 27.000684462696782, "x_mean": 13.500342231348395, "sxx": 1827.0287638303862 }, "models": [ { "key": "ols", "name": "Linear trend (OLS)", "ok": true, "r2": 0.556001341249994, "aic": 90.70156759956924, "k": 2, "residual_sd": 4.880737154174884, "slope_per_year": -0.6515481141091662, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.22592142651080618, "n": 28, "effective_n": 17.679925963433163, "se_inflation": 1.2584581992244608, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 6.528022353852593, "mae": 5.618453227504223 } }, { "key": "theilsen", "name": "Linear — Theil–Sen (robust)", "ok": true, "r2": 0.525788308768505, "aic": 92.54487161808568, "k": 2, "residual_sd": 5.044065520602068, "slope_per_year": -0.546014079472285, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.21614367395904172, "n": 28, "effective_n": 18.047190968561512, "se_inflation": 1.2455874067003143, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 5.672109238238815, "mae": 4.642072508287278 } }, { "key": "quadratic", "name": "Quadratic (curvature)", "ok": true, "r2": 0.6667731450473577, "aic": 84.66562255089474, "k": 3, "residual_sd": 4.312024875500472, "slope_per_year": 0.43733904645828514, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.12670199175616897, "n": 28, "effective_n": 21.70258365542948, "se_inflation": 1.135856051597631, "material": false }, "validation": { "ok": true, "holdout": 6, "rmse": 5.186811735925682, "mae": 4.864620703480612 } }, { "key": "loglinear", "name": "Log-linear (exponential)", "ok": true, "r2": 0.5860769233037232, "aic": 88.73760867636639, "k": 2, "residual_sd": 4.712533066829717, "slope_per_year": null, "growth_rate_per_year": -0.022879774706820455, "autocorrelation": { "ok": true, "lag1": 0.1771402889299939, "n": 28, "effective_n": 19.572919325447028, "se_inflation": 1.1960551699615198, "material": false }, "validation": { "ok": true, "holdout": 6, "rmse": 4.735984841255481, "mae": 3.547982605901824 } }, { "key": "holt", "name": "Holt exponential smoothing", "ok": true, "r2": 0.36949971455209163, "aic": 100.52094546277705, "k": 2, "residual_sd": 5.816171910013006, "slope_per_year": -1.0719610128777282, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": -0.017694313990010712, "n": 28, "effective_n": 28.0, "se_inflation": 1.0, "material": false }, "validation": { "ok": true, "holdout": 6, "rmse": 5.713913064489759, "mae": 4.6405502713274105 } }, { "key": "mean", "name": "Mean (reference)", "ok": true, "r2": 0.0, "aic": 111.43571224642749, "k": 1, "residual_sd": 7.18785624446913, "slope_per_year": null, "growth_rate_per_year": null, "autocorrelation": { "ok": true, "lag1": 0.5089909653841577, "n": 28, "effective_n": 9.110891506062506, "se_inflation": 1.75306725658367, "material": true }, "validation": { "ok": true, "holdout": 6, "rmse": 7.178928755777682, "mae": 6.545581846382267 } } ], "best_model": "loglinear", "best_by": "holdout_rmse", "selected_model": "ols", "holdout": 6, "trend": { "ok": true, "model": "ols", "n": 28, "slope_per_year": -0.6515481141091662, "growth_rate_per_year": null, "r2": 0.556001341249994, "residual_sd": 4.880737154174884, "slope_se": 0.1436982642319584, "slope_se_naive": 0.11418596527124547, "sen_interval": null, "autocorrelation": { "ok": true, "lag1": 0.22592142651080618, "n": 28, "effective_n": 17.679925963433163, "se_inflation": 1.2584581992244608, "material": true }, "dof": 26, "significant_95": true, "fragile": false, "span_years": 27.000684462696782, "x0_timestamp": 788918400 }, "mann_kendall": { "ok": true, "s": -228, "z": -4.484729924761178, "significant_95": true, "direction": "decreasing" }, "agreement": "agree", "change_point": { "ok": true, "index": 12, "label": "2007", "timestamp": 1167609600, "k": 155, "p_value": 0.0035280209736769934, "significant_95": true, "confounded_by_trend": true, "note": "The series has a significant monotonic trend. On trending series the Pettitt test tends to place the break near the middle of the series; this result is not on its own evidence of a genuine regime change should not be counted as evidence; it should be read as a side effect of the trend." }, "fit_gap": { "last_observed": 19.804863481228672, "fitted_at_last": 17.640021497586595, "gap": 2.164841983642077, "gap_in_sd": 0.4435481598902155, "large": false }, "scenarios": [], "stationarity": { "ok": true, "adf": { "ok": true, "statistic": -2.0285488723371152, "lags": 5, "trend_included": true, "critical": { "1%": -3.96, "5%": -3.41, "10%": -3.12 }, "reject_unit_root_5pct": false, "interpretation": "The unit root could not be rejected: the series is probably not stationary. Differencing (d≥1) may be needed.", "note": "The null hypothesis is THAT A UNIT ROOT EXISTS. Failing to reject it does not prove non-stationarity; the test has low power on short series It must therefore be read together with KPSS." }, "kpss": { "ok": true, "statistic": 0.1466983479855278, "lags": 2, "trend_included": true, "critical": { "1%": 0.216, "5%": 0.146, "10%": 0.119 }, "reject_stationarity_5pct": true, "interpretation": "Stationarity is rejected at the 5% level: the series is not stationary.", "note": "The null hypothesis is STATIONARITY; it is set the opposite way round from ADF. The two must be read together." }, "verdict": "non_stationary", "interpretation": "Both tests show non-stationarity: differencing (ARIMA d≥1) is required." }, "residual_diagnostics": { "ok": true, "ljung_box": { "ok": true, "statistic": 3.993513179954617, "lags": 6, "dof": 4, "p_value": 0.4068844571812298, "residuals_are_white_5pct": true, "acf": [ { "lag": 1, "r": 0.22592142651080618 }, { "lag": 2, "r": -0.10375699013490589 }, { "lag": 3, "r": -0.03502747148721232 }, { "lag": 4, "r": 0.1753539301228806 }, { "lag": 5, "r": -0.05066742036385763 }, { "lag": 6, "r": -0.14890378039006275 } ], "interpretation": "The residuals are consistent with white noise: the model appears to have left no capturable structure in the series.", "note": "Degrees of freedom are reduced by the number of fitted parameters; otherwise the test favours the model that produced the residuals." }, "jarque_bera": { "ok": true, "statistic": 10.52114682180679, "skewness": 1.1259483180707905, "kurtosis": 4.9867278090863865, "excess_kurtosis": 1.9867278090863865, "p_value": 0.0051923265282781, "normal_5pct": false, "interpretation": "The residuals are not normally distributed. Analytical confidence intervals rest on that assumption; the block bootstrap interval is more reliable on this series.", "shape": "right-skewed" }, "breusch_pagan": { "ok": true, "statistic": 6.0018145408281, "p_value": 0.013721166619092312, "homoscedastic_5pct": false, "slope_of_variance": -0.08105570568055423, "interpretation": "The residual spread is SHRINKING over time. A single uncertainty band understates the start of the series." }, "violations": [ "non-normal distribution", "changing variance" ], "verdict": "Assumption(s) violated: non-normal distribution, changing variance. In that case read the block bootstrap interval rather than the analytical confidence intervals." }, "theilsen": { "ok": true, "model": "theilsen", "n": 28, "slope_per_year": -0.546014079472285, "growth_rate_per_year": null, "r2": 0.525788308768505, "residual_sd": 5.044065520602068, "slope_se": 0.11996549121320714, "slope_se_naive": 0.09631238287083178, "sen_interval": { "lower": -0.7787692687033061, "upper": -0.4012247278496455, "se_equivalent": 0.09631238287083178 }, "autocorrelation": { "ok": true, "lag1": 0.21614367395904172, "n": 28, "effective_n": 18.047190968561512, "se_inflation": 1.2455874067003143, "material": true }, "dof": 26, "significant_95": true, "fragile": false, "span_years": 27.000684462696782, "x0_timestamp": 788918400 }, "naive_holdout": { "n": 6, "rmse": 3.8519208924267656 }, "change_percent": -60.665613741353184 } } ] }