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Three Essays on Non-stationary Time Series

Xiaoye Li

出版	Pennsylvania State University, 2013
URL	http://books.google.com.hk/books?id=X8lWAQAACAAJ&hl=&source=gbs_api

註釋

We study statistical inference for a class of non-stationary time series with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods that are developed for stationary or locally stationary time series are not applicable. Based on a self-normalization technique, we address several inference problems, including self-normalized Central Limit Theorem, self-normalized cumulative sum test for change-point problem, long-run variance estimation through blockwise self-normalization, and self-normalization based wild bootstrap for non-stationary time series. Monte Carlo simulation studies show that the proposed self-normalization based methods outperform stationarity based alternatives. We demonstrate the proposed methodology using two real data sets: annual mean precipitation rates in Seoul during 1771--2000, and quarterly U.S. Gross National Product growth rates during 1947--2002.^