Asymptotic Inferences for an AR(1) Model with a Change Point: Stationary and Nearly Non-stationary Cases

Pang, Tianxiao and Zhang, Danna and Chong, Terence Tai-Leung (2013): Asymptotic Inferences for an AR(1) Model with a Change Point: Stationary and Nearly Non-stationary Cases. Published in: Journal of Time Series Analysis , Vol. 2, No. 35 (1 March 2014): pp. 133-150.

Abstract

This paper examines the asymptotic inference for AR(1) models with a possible structural break in the AR parameter β near the unity at an unknown time k₀. Consider the model y_{t}=β₁y_{t-1}I{t≤k₀}+β₂y_{t-1}I{t>k₀}+ε_{t}, t=1,2,⋯,T, where I{⋅} denotes the indicator function. We examine two cases: Case (I) |β₁|<1,β₂=β_{2T}=1-c/T; and case (II) β₁=β_{1T}=1-c/T,|β₂|<1, where c is a fixed constant, and {ε_{t},t≥1} is a sequence of i.i.d. random variables which are in the domain of attraction of the normal law with zero means and possibly infinite variances. We derive the limiting distributions of the least squares estimators of β₁ and β₂, and that of the break-point estimator for shrinking break for the aforementioned cases. Monte Carlo simulations are conducted to demonstrate the finite sample properties of the estimators. Our theoretical results are supported by Monte Carlo simulations

• Item Type: MPRA Paper
• Language: English
• Keywords: AR(1) model, Change point, Domain of attraction of the normal law, Limiting distribution, Least squares estimator.
• 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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