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• W4300680602 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." @default.
• W4300680602 created "2022-10-04" @default.
• W4300680602 creator A5013357244 @default.
• W4300680602 creator A5070248488 @default.
• W4300680602 date "2006-10-01" @default.
• W4300680602 modified "2023-09-27" @default.
• W4300680602 title "Averaging Forecasts from VARs with Uncertain Instabilities" @default.
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• W4300680602 doi "https://doi.org/10.17016/feds.2007.42" @default.
• W4300680602 hasPublicationYear "2006" @default.
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