Forecasting with difference-stationary and trend-stationary models
AbstractWhile 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.
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Bibliographic InfoArticle provided by Royal Economic Society in its journal The Econometrics Journal.
Volume (Year): 4 (2001)
Issue (Month): 1 ()
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Other versions of this item:
- Clements, M.P. & Hendry, D.P., 1998. "Forecasting with Difference-Stationary and Trend-Stationary Models," The Warwick Economics Research Paper Series (TWERPS) 516, University of Warwick, Department of Economics.
- David Hendry & Michael P. Clements, 2000. "Forecasting with Difference-Stationary and Trend-Stationary Models," Economics Series Working Papers 5, University of Oxford, Department of Economics.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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