Сравнение методов

	Байесовский ARDL-тест на коинтеграцию	Модель Байесовского векторного авторегрессионного анализа (BVAR)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2001 (ARDL); Bayesian extension 2010s	1984
Автор метода	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature	Doan, Litterman & Sims
Тип	Cointegration / bounds testing	Multivariate time-series model
Основополагающий источник	Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.
Другие названия	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Связанные	5	5
Сводка	The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one.	The Bayesian Vector Autoregression (BVAR) model extends the classical VAR framework by incorporating prior beliefs about the model coefficients. Priors — most commonly the Minnesota prior — shrink VAR coefficients toward economically sensible values, dramatically reducing overfitting and improving out-of-sample forecast accuracy even when the number of variables is large.