Bayesian Estimation and Hypothesis Testing of Identified Normalized VAR Models
نویسنده
چکیده
In this paper, Bayesian estimation and hypothesis testing are introduced for identified normalized Vector Autoregressive (VAR) models. A class of priors is proposed to take advantage of the structure of normalized VAR models. Efficient Markov Chain Monte Carlo algorithms are used for sampling from the posterior of the VAR parameters without using Metropolis algorithms. Marginal likelihoods are computed via Chib’s (1995) methods for hypothesis testing based on Bayes factors. Numerical simulations show that when the sample size is small, the commonly used Schwarz criterion for testing of competing VAR models deviates from the Bayes factor and is not as effective in selecting the correct model. The method developed in the study is applied to a real data set to test competing models on the macroeconomic, state-, and sector-specific effects of employment growth.
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