Estimating Multiple Breaks in Nonstationary Autoregressive Models

Pang, Tianxiao and Du, Lingjie and Chong, Terence Tai Leung (2018): Estimating Multiple Breaks in Nonstationary Autoregressive Models.

Abstract

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, where 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 if 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), Phillips et al. (2011) and Phillips et al. (2015a, 2015b). We provide an estimation procedure as well as the asymptotic theory for the model.

• Item Type: MPRA Paper
• Language: English
• Keywords: Change point, Financial bubble, Least squares estimator, Mildly explosive, Mildly integrated.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

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