This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 423
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract - #308039
Title: Segmenting Nonstationary Time Series via Quantile Autoregressions
Author(s): Ming Zhong*+
Companies: University of California, Davis
Address: 2900 Solano Park Circle #3424, Davis, CA, 95616,
Keywords: non-stationarity ; change points ; quantile autoregression ; likelihood-ratio-type test

Identifying and locating structural breaks has become an important issue in time series, since parameter estimates and forecasts can be severely biased if non-stationarities are not taken into account. In our work, we try to detect structural breaks within quantile autoregression models. As a member of the class of random-coefficient models, quantile autoregressions allow the autoregressive coefficients to take distinct values over different quantiles of the innovation process, and may thus expand the modeling options for non-stationary and asymmetric time series. We propose a likelihood-ratio-type test for multiple structural breaks and will present its asymptotic properties. Numerical results from simulations and economic data applications, based on the principle of dynamic programming, show that the procedure is consistent for estimating the number and location of the breaks.

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