| 主讲人简介： | Prof. Terence Chong is Associate Head of New Asia College, Executive Director of Lau Chor Tak Institute of Global Economics, Finance, and Co-Director of Global Economics and Finance Program and professor of Economics, The Chinese University of Hong Kong. He served as Siyuan Chair Professor of Nanjing University in China from 2013 to 2016. Prof Chong received his Bachelor degree in Economics from The Chinese University of Hong Kong in 1991, and Ph.D. in Economics from the University of Rochester in 1995. His main research area is financial econometrics. He has published over 1000 articles in international journals and newspapers covering a wide spectrum of topics in Econometrics, Finance, Mathematics, Psychology, Education and the Chinese Economy. Prof. Chong ranks top 37th worldwide in theoretical econometrics,top 1% in terms of number of distinct works and top 5% economists worldwide (RePEc). His papers are published in reputable international journals, including Journal of Econometrics, Econometric Theory, Econometric Review, Econometrics Journal, Journal of Time Series Analysis, Journal of Economic Dynamics and Control, Journal of Banking and Finance and Financial Management etc.. Prof. Chong is dedicated to community service. He is the associate editor of Singapore Economic Review and Economics Bulletin, and the director of the Financial Markets Program, Hong Kong Institute of Asia-Pacific Studies. |
| 讲座简介： | Chong (1995) and Bai (1997) proposed a sample-splitting method to estimate a multiple break model. However, their studies focused on stationary time series models, in which the identification of the first break depends on the magnitude and the duration of the break, and a testing procedure is needed to assist the estimation of the remaining breaks in subsamples split by the break points found earlier. In this paper, we focus on nonstationary multiple-break autoregressive models. Unlike the stationary case, we show that the duration of a break does not affect whether it will be identified first. Rather, it depends on the stochastic order of magnitude of signal strength of the break under the case of constant break magnitude and also the square of the magnitude of the break under the case of shrinking break magnitude. Since the subsamples usually have different stochastic orders in nonstationary autoregressive models with breaks, one can therefore determine which break will be identified first. We apply this finding to the models proposed in Phillips and Yu (2011) and Phillips et al. (2011, 2015a, 2015b). We propose an estimation procedure as well as the asymptotic theory for the model. Some extensions to more general models are provided, and the hypothesis test with the null hypothesis being the unit root model is examined. Results of numerical simulations and an empirical study are given to illustrate the finite-sample performance.
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