Combining forecasts from nested models

Todd Clark and Michael McCracken

No 2007-43, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: 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 data generating process 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. Monte Carlo and empirical analyses verify the practical effectiveness of our combination approach.

Keywords: Econometric models; Economic forecasting (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Combining Forecasts from Nested Models* (2009)
Working Paper: Combining forecasts from nested models (2008)
Working Paper: Combining forecasts from nested models (2006)
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