Combining forecasts from nested models
by Todd E. Clark
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.
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| Edition | Availability |
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Combining forecasts from nested models
2006, Research Division, Federal Reserve Bank of Kansas City
electronic resource /
in English
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2
Combining forecasts from nested models
2006, Research Division, Federal Reserve Bank of Kansas City
electronic resource /
in English
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Edition Notes
Title from PDF file (viewed on Mar. 29, 2006).
"March 2006."
Includes bibliographical references.
Also available in print.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
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