Piecewise Quantile Autoregressive Modeling for Nonstationary Time Series

• Activity Number: 285
• Type: Invited
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Alexander Aue*+ and Thomas C.M. Lee and Ming Zhong
• Companies: UC Davis and UC Davis and UC Davis
• Keywords: Time Series ; Model selection ; Genetic algorithm ; Quantile regression ; Structural breaks

Abstract

We a new methodology is presented for the ?tting of non-stationary time series that exhibit nonlinearity, asymmetry, local persistence and changes in location, scale and shape of the underlying distribution. In order to achieve this goal, model selection is performed in the class of piecewise stationary quantile autoregressive processes. The best model is de?ned in terms of minimizing a minimum description length criterion derived from an asymmetric Laplace likelihood. Its practical minimization is done with the use of genetic algorithms. If the data generating process follows indeed a piecewise quantile autoregression structure, it is shown that the proposed method is consistent for estimating the break points and the autoregressive parameters. Empirical work suggests that the proposed method performs well in ?nite samples.