Sammenlign metoder

	Bayesiansk VAR-model (BVAR)	Bayesian ARDL Bounds Test
Fagområde	Økonometri	Økonometri
Familie	Regression model	Regression model
Oprindelsesår	1984	2001 (ARDL); Bayesian extension 2010s
Ophavsperson	Doan, Litterman & Sims	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature
Type	Multivariate time-series model	Cointegration / bounds testing
Oprindelig kilde	Doan, T., Litterman, R., & Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1), 1–100.	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.
Aliasser	BVAR, Bayesian VAR, Bayesian vector autoregressive model, BVAR model	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test
Relaterede	5	5
Resumé	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.	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.