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A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP

Michael Clements and Hans-Martin Krolzig ()

Econometrics Journal, 1998, vol. 1, issue ConferenceIssue, C47-C75

Abstract: While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two methods of analysis are employed: an empirical forecast accuracy comparison of the two models, and a Monte Carlo study. The latter allows us to control for factors that may otherwise undermine the performance of the non-linear models.

Keywords: Business cycles; Monte Carlo simulation; Nonlinear time series; Prediction; Regime shifts. (search for similar items in EconPapers)
Date: 1998
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Working Paper: A COMPARISON OF THE FORECAST PERFORMANCE OF MARKOV-SWITCHING AND THRESHOLD AUTOREGRESSIVE MODELS OF US GNP (1997) Downloads
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Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

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