Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure

Antonio Pacifico
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Antonio Pacifico: Department of Economics and Law, University of Macerata, Piazza Strambi 1, 62100 Macerata, Italy

Econometrics, 2022, vol. 10, issue 3, 1-24

Abstract: This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis, volatility changes and conditional density forecasts are addressed ensuring accurate predictive performance and capability. An empirical and simulated experiment are developed to highlight and discuss the functioning of the estimating procedure and forecasting accuracy.

Keywords: structural panel VAR; bayesian model averaging; compressed regression methods; markov chains algorithms; forecasting; stochastic volatility (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2022
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