Title

Forecasts in a Slightly Misspecified Finite Order VAR Model

Author(s)

Ulrich K. Müller Ulrich Müller (Princeton University)

James H. Stock James Stock (Harvard University)

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.

Creation Date

2011-07

Section URL ID

Paper Number

2011-4

URL

http://www.princeton.edu/~umueller/ssforecast.pdf

File Function

Jel

C53; C32; C11

Keyword(s)

Spectral Domain Prior; Posterior Approximation; Information Criteria

Suppress

false

Series

13