Forecasting autoregressive time series under changing persistenceCreation-Date: 20100701
AbstractChanging persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable recommendations, no matter whether a change in persistence occurs or not. Seven different forecasting strategies based on a biasedcorrected estimator are compared by means of a large-scale Monte Carlo study. The results for decreasing and increasing persistence are highly asymmetric and new to the literature. Its good predictive ability and its balanced performance among different settings strongly advocate the use of forecasting strategies based on the Bai-Perron procedure.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-28.
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Forecasting; changing persistence; structural break; pre-testing; breakpoint estimation; bias-correction;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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