The performance of alternative forecasting methods for SETAR models
Clements, Michael P. and Smith, Jeremy (Jeremy P.) (1996) The performance of alternative forecasting methods for SETAR models. Working Paper. Coventry: University of Warwick, Department of Economics. Warwick economic research papers (No.467).
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Official URL: http://www2.warwick.ac.uk/fac/soc/economics/resear...
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.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||H Social Sciences > HB Economic Theory|
|Divisions:||Faculty of Social Sciences > Economics|
|Library of Congress Subject Headings (LCSH):||Economic forecasting, Threshold logic, Simulation games, Monte Carlo method|
|Series Name:||Warwick economic research papers|
|Publisher:||University of Warwick, Department of Economics|
|Place of Publication:||Coventry|
|Official Date:||June 1996|
|Number of Pages:||35|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
|Funder:||Economic and Social Research Council (Great Britain) (ESRC)|
|Grant number:||L116251015 (ESRC)|
Al-Qassam, M. S. and J. A. Lane, 1989, Forecasting exponential autoregressive models of order 1, Journal of Time Series Analysis, 10, 95-113.
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