Forecasts in a Slightly Misspecified Finite Order VAR Model

Ulrich Müller and James Stock
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Ulrich Müller: Princeton University
James Stock: Harvard University

Working Papers from Princeton University. Economics Department.

Abstract: We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes the problem asymptotically a normal-normal Bayes problem, resulting in closed-form solutions for the best forecast. When applied to data on 132 U.S. monthly macroeconomic time series, the method is found to improve upon autoregressive forecasts by an amount consistent with the theoretical and Monte Carlo calculations.

Keywords: Spectral Domain Prior; Posterior Approximation; Information Criteria (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Date: 2011-07
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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2011-4

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