Asymptotic inferences for an AR(1) model with a change point: stationary and nearly non-stationary cases

2013/06/06 by Tianxiao, Pang, Danna, Zhang, Tai-Leung, Chong Terence
#60F05 #62F12 #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST)

paper · doi:10.48550/arxiv.1306.1294

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-1I\t≤ k₀\+β₂y_t-1I\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.\newline

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