Порівняння методів

	Модель ARIMA (Авторегресійна інтегрована ковзна середня)	Бутстреп-інференс	Тест Дібольда-Маріано на рівність прогнозної точності
Галузь	Економетрика	Статистика	Економетрика
Родина	Regression model	Regression model	Hypothesis test
Рік появи	2015	1979	1995
Автор методу	Box & Jenkins (Box-Jenkins methodology)	Bradley Efron	Francis Diebold & Roberto Mariano
Тип	Univariate time-series model	Resampling-based inference	Non-parametric forecast comparison test
Основоположне джерело	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.	Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.
Інші назви	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
Пов'язані	5	5	3
Підсумок	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.