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Averaging Forecasts from VARs with Uncertain
Instabilities By Todd E. Clark and
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 just 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 and the Survey of
Professional Forecasters as benchmarks. Keywords: Forecast combination, real-time data, prediction, structural
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