The Performance of Alternative Forecasting Methods for SETAR Models

Michael Clements and Jeremy Smith

No 268737, Economic Research Papers from University of Warwick - Department of Economics

Abstract: We compare a number of methods that have been proposed in the literature for obtaining h-step ahead minimum mean square error forecasts for SETAR models. These forecasts are compared to those from an AR model. The comparison of forecasting methods is made using Monte Carlo simulation. The Monte Carlo method of calculating SETAR forecasts is generally at least as good as that of the other methods we consider. An exception is when the disturbances in the SETAR model come from a highly asymmetric distribution, when a Bootstrap method is to be preferred. An empirical application calculates multi-period forecasts from a SETAR model of US GNP using a number of the forecasting methods. We find that whether there are improvements in forecast performance relative to a linear AR model depends on the historical epoch we select, and whether forecasts are evaluated conditional on the regime the process was in at the time the forecast was made.

Keywords: Agricultural and Food Policy; Production Economics; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 35
Date: 1996-06-06
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: The performance of alternative forecasting methods for SETAR models (1997)
Working Paper: Performance of Alternative Forecasting Methods for Setar Models (1996)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:uwarer:268737

DOI: 10.22004/ag.econ.268737

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