Combining forecasts from nested models
By: Todd E. Clark and Michael W. McCracken
Contributors: McCracken, Michael W.; Federal Reserve Bank of Kansas City. Research Division
Series: RWP -- 06-02; Research working paper (Federal Reserve Bank of Kansas City : Online) -- 06-02.
Publisher: Research Division, Federal Reserve Bank of Kansas City
Published: 2006
Place of publication: Kansas City [Mo.]
Language: eng
Format: [electronic resource] /
Description: Motivated by the common finding that linear autoregressive models forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but as the sample size grows, the DGP converges to the restricted model. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. In the Monte Carlo and empirical analysis, we compare the effectiveness of our combination approach against related alternatives, such as Bayesian estimation.
Notes: Title from PDF file (viewed on May 31, 2006).

"March 2006."

Includes bibliographical references.

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Mode of access: World Wide Web.