Compară metode

	Testul Bayesian ARDL de Limite	Modelul Vector Autoregresiv Bayesian (BVAR)
Domeniu	Econometrie	Econometrie
Familie	Regression model	Regression model
Anul apariției	2001 (ARDL); Bayesian extension 2010s	1984
Autorul original	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature	Doan, Litterman & Sims
Tip	Cointegration / bounds testing	Multivariate time-series model
Sursa seminală	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.
Denumiri alternative	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model
Înrudite	5	5
Rezumat	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.