Forecasts in a slightly misspecified finite order var
By: Ulrich K. Müller, James H. Stock
Contributors: Stock, James H.; National Bureau of Economic Research
Series: NBER working paper series -- working paper 16714; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 16714.
Publisher: National Bureau of Economic Research
Published: 2011
Place of publication: Cambridge, MA
Language: eng
Format: [electronic resource] /
Description: "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/&radic;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"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 4/28/2011.

Includes bibliographical references.

Also available in print.

System requirements: Adobe Acrobat Reader.

Mode of access: World Wide Web.