Forecasting in the Presence of Level Shifts

This 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.


Issue Date:
2004
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/11985
Total Pages:
35
Series Statement:
Working Paper No. 04-014




 Record created 2017-04-01, last modified 2017-08-18

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