Forecasting in the Presence of Level Shifts
AbstractThis article addresses the problem of forecasting time series that are subject to level shifts. Processes with level shifts possess a nonlinear dependence structure. Using the stochastic permanent breaks (STOPBREAK) model, I model this nonlinearity in a direct and flexible way that avoids imposing a discrete regime structure. I apply this model to the rate of price inflation in the United States, which I show is subject to level shifts. These shifts significantly affect the accuracy of out-of-sample forecasts, causing models that assume covariance stationarity to be substantially biased. Models that do not assume covariance stationarity, such as the random walk, are unbiased but lack precision in periods without shifts. I show that the STOPBREAK model outperforms several alternative models in an out-of-sample inflation forecasting experiment.
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Bibliographic InfoPaper provided by University of California, Davis, Department of Agricultural and Resource Economics in its series Working Papers with number 11985.
Date of creation: 2004
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Financial Economics; Research Methods/ Statistical Methods;
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