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| ARIMA (Autoregressive Integrated Moving Average) Modell× | Bootstrapinferens× | Diebold-Mariano-testet för lika prediktiv noggrannhet× | |
|---|---|---|---|
| Ämnesområde≠ | Ekonometri | Statistik | Ekonometri |
| Familj≠ | Regression model | Regression model | Hypothesis test |
| Ursprungsår≠ | 2015 | 1979 | 1995 |
| Upphovsperson≠ | Box & Jenkins (Box-Jenkins methodology) | Bradley Efron | Francis Diebold & Roberto Mariano |
| Typ≠ | Univariate time-series model | Resampling-based inference | Non-parametric forecast comparison test |
| Ursprungskälla≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. DOI ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi |
| Närliggande≠ | 5 | 5 | 3 |
| Sammanfattning≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. | The Diebold-Mariano (DM) test, introduced by Diebold and Mariano in 1995, is a widely used non-parametric procedure for formally comparing the predictive accuracy of two competing forecasting models. It evaluates whether the difference in forecast errors between two models is statistically significant, without requiring nested models or specific distributional assumptions about the forecasts, making it broadly applicable across economics, finance, and time-series analysis. |
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