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Averaging forecasts from VARs with uncertain instabilities

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Author Info
Todd E. Clark
Michael W. McCracken

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Abstract

A body of recent work suggests commonly-used VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, different observation windows for estimation, (over-) differencing, intercept correction, stochastically time-varying parameters, break dating, discounted least squares, Bayesian shrinkage, and detrending of inflation and interest rates. Although each individual method could be useful, the uncertainty inherent in any single representation of instability could mean that combining forecasts from the entire range of VAR estimates will further improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combination in improving VAR forecasts made with real-time data. The combinations include simple averages, medians, trimmed means, and a number of weighted combinations, based on: Bates-Granger regressions, factor model estimates, regressions involving forecast quartiles, Bayesian model averaging, and predictive least squares-based weighting. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models as benchmarks.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2007-42.

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Date of creation: 2007
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Handle: RePEc:fip:fedgfe:2007-42

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Keywords: Econometric models Economic forecasting

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  6. Antonello D’Agostino & Domenico Giannone & Paolo Surico, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 605, European Central Bank. [Downloadable!]
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  7. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City. [Downloadable!]
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  21. Todd E. Clark & Michael W. McCracken, 2006. "Combining forecasts from nested models," Research Working Paper RWP 06-02, Federal Reserve Bank of Kansas City. [Downloadable!]
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  22. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Pesaran, M.H. & Pick, A., 2008. "Forecasting Random Walks Under Drift Instability," Cambridge Working Papers in Economics 0814, Faculty of Economics, University of Cambridge. [Downloadable!]
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  2. John M Maheu & Thomas H McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Papers tecipa-293, University of Toronto, Department of Economics. [Downloadable!]
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