The performance of alternative forecasting methods for SETAR models
Clements, Michael P. and Smith, Jeremy (Jeremy P.). (1997) The performance of alternative forecasting methods for SETAR models. International Journal of Forecasting, Volume 13 (Number 4). pp. 463-475. ISSN 0169-2070Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/S0169-2070(97)00017-4
We compare a number of methods that have been proposed in the literature for obtaining h-step ahead minimum mean square error forecasts for self-exciting threshold autoregressive (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 gross national product 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. (C) 1997 Elsevier Science B.V.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HC Economic History and Conditions
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
|Divisions:||Faculty of Social Sciences > Economics|
|Journal or Publication Title:||International Journal of Forecasting|
|Number of Pages:||13|
|Page Range:||pp. 463-475|
|Access rights to Published version:||Restricted or Subscription Access|
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