Combining forecasts from nested models

Todd Clark and Michael McCracken

No RWP 06-02, Research Working Paper from Federal Reserve Bank of Kansas City

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 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.

Keywords: Forecasting (search for similar items in EconPapers)
Date: 2006
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (7)

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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 (2007)
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