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ALTERNATIVE BAYESIAN COMPRESSION IN VECTOR AUTOREGRESSIONS AND RELATED MODELS

Mike G. Tsionas

Athens University of Economics and Business

ABSTRACT

In this paper we reconsider large Bayesian Vector Autoregressions (BVAR) from the point of view of Bayesian Compressed Regression (BCR). First, we show that there are substantial gains in terms of out-of-sample forecasting by treating the problem as an error-in-variables formulation and estimating the compression matrix instead of using random draws. As computations can be efficiently organized around a standard Gibbs sampler, timings and computa-tional complexity are not affected severely. Second, we extend the Multivariate Autoregressive Index model to the BCR context and show that we have, again, gains in terms of out-of-sample forecasting. The new techniques are used in U.S data featuring medium-size, large and huge BVARs.

Keywords: Bayesian Vector Autoregressions; Bayesian Compressed Re-gression; Error-in-Variables; Forecasting; Multivariate Autoregressive Index model.

JEL Classifications: C11, C13.

Acknowledgements: The author wishes to thank the Bank of Greece for its hospitality and funding the research in this paper. Excellent research assistance by Xingzhi Yao is gratefully acknowledged.. The views of the paper are of the author and do not necessarily reflect those of the Bank of Greece.

Correspondence:
E. G. Tsionas
Athens University of Economics and Business
76 Patission Str.
10434 Athens,Greece
email: tsionas@otenet.gr


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