Forecasts in a Slightly Misspecified Finite Order VAR

Ulrich K. Müller and James H. Stock

No 16714, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C11 C22 C32 (search for similar items in EconPapers)
Date: 2011-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
Note: TWP
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Citations: View citations in EconPapers (1)

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