Forecasts in a Slightly Misspecified Finite Order VAR
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.
|Date of creation:||Jan 2011|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.nber.org
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:16714. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.