Combining forecasts from nested models
Abstract
Motivated by the common finding that linear autoregressive models often 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 a subset of the coefficients are treated as being local-to-zero. 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 e effectiveness of our combination approach.Download Info
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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2008-037.Length:
Date of creation: 2008
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Handle: RePEc:fip:fedlwp:2008-037
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Keywords: Econometric models ; Economic forecasting;Other versions of this item:
- Todd E. Clark & Michael W. McCracken, 2009. "Combining Forecasts from Nested Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 303-329, 06.
- Todd E. Clark & Michael W. McCracken, 2006. "Combining forecasts from nested models," Research Working Paper RWP 06-02, Federal Reserve Bank of Kansas City.
- Todd E. Clark & Michael W. McCracken, 2007. "Combining forecasts from nested models," Finance and Economics Discussion Series 2007-43, Board of Governors of the Federal Reserve System (U.S.).
- NEP-ALL-2008-11-04 (All new papers)
- NEP-ETS-2008-11-04 (Econometric Time Series)
- NEP-FOR-2008-11-04 (Forecasting)
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Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
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