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Forecasting with difference-stationary and trend-stationary models

Michael Clements and David Hendry ()

Econometrics Journal, 2001, vol. 4, issue 1, S1-S19

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: 2001
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Related works:
Working Paper: Forecasting with Difference-Stationary and Trend-Stationary Models (2000) Downloads
Working Paper: FORECASTING WITH DIFFERENCE-STATIONARY AND TREND-STATIONARY MODELS (1998) Downloads
Working Paper: Forecasting with Difference-Stationary and Trend-Stationary Models (1998) 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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