Alternative Bayesian compression in Vector Autoregressions and related models

Mike Tsionas

No 216, Working Papers from Bank of Greece

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 e?ciently organized around a standard Gibbs sampler, timings and computa-tional complexity are not a?ected 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. (search for similar items in EconPapers)
JEL-codes: C11 C13 (search for similar items in EconPapers)
Pages: 40
Date: 2016-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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