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| Mô hình ARIMA (Autoregressive Integrated Moving Average)× | Suy luận Bootstrap× | Kiểm định Diebold-Mariano về Độ chính xác Dự báo Tương đương× | |
|---|---|---|---|
| Lĩnh vực≠ | Kinh tế lượng | Thống kê | Kinh tế lượng |
| Họ≠ | Regression model | Regression model | Hypothesis test |
| Năm ra đời≠ | 2015 | 1979 | 1995 |
| Người khởi xướng≠ | Box & Jenkins (Box-Jenkins methodology) | Bradley Efron | Francis Diebold & Roberto Mariano |
| Loại≠ | Univariate time-series model | Resampling-based inference | Non-parametric forecast comparison test |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác≠ | 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 |
| Liên quan≠ | 5 | 5 | 3 |
| Tóm tắt≠ | 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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