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on Forecasting |
| By: | Todd E. Clark; Michael W. McCracken |
| Abstract: | Small-scale VARs are widely used in macroeconomics for forecasting U.S. output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters and the Federal Reserve Board's Greenbook as benchmarks. |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2007-41&r=for |
| By: | Todd E. Clark; Michael W. McCracken |
| 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. |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2007-42&r=for |
| By: | Giuseppe Arbia; Marco Bee; Giuseppe Espa |
| Abstract: | In this paper we compare the relative efficiency of different forecasting methods of space-time series when variables are spatially and temporally correlated. We consider the case of a space-time series aggregated into a single time series and the more general instance of a space-time series aggregated into a coarser spatial partition. We extend in various directions the outcomes found in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. The outcomes obtained provide operational suggestions on how to choose between alternative forecasting methods in empirical circumstances. |
| Keywords: | Spatial correlation, Aggregation, Forecast efficiency, Space–time models, Edge effects, Negative spatial correlation. |
| JEL: | C15 C21 C43 C53 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:trn:utwpde:0720&r=for |