Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Тест Джакоміні-Вайта на умовну прогностичну здатність× | Тест Дібольда-Маріано на рівність прогнозної точності× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Hypothesis test | Hypothesis test |
| Рік появи≠ | 2006 | 1995 |
| Автор методу≠ | Raffaella Giacomini & Halbert White | Francis Diebold & Roberto Mariano |
| Тип≠ | Non-nested forecast comparison test | Non-parametric forecast comparison test |
| Основоположне джерело≠ | Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578. DOI ↗ | Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. DOI ↗ |
| Інші назви | GW Test, Conditional Predictive Ability Test, Giacomini-White CPA Test, Koşullu Tahmin Yeteneği Testi | DM Test, Test of Equal Forecast Accuracy, Diebold-Mariano Forecast Comparison Test, Tahmin Doğruluğu Eşitliği Testi |
| Пов'язані | 3 | 3 |
| Підсумок≠ | The Giacomini-White (GW) test, introduced by Raffaella Giacomini and Halbert White in 2006, evaluates whether two competing forecasting methods have equal conditional predictive ability given information available at the time of forecast. Unlike unconditional tests such as the Diebold-Mariano test, it asks whether one method systematically outperforms the other in specific economic or market conditions, making it especially useful for practitioners who need state-dependent forecast comparisons. | 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. |
| ScholarGateНабір даних ↗ |
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