Estimation of Change-Point for a Class of Count Time Series Models

• Activity Number: 175 - Statistical Modeling
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Yunwei Cui* and Rongning Wu and Qi Zheng
• Companies: Towson University and Baruch College, The City University of New York, New York and University of Louisville
• Keywords: time series of count; off-line change-point estimation

Abstract

We studied the off-line (retrospective) change-point estimation for a class of observation-driven models for count data. Under regularity conditions, when the magnitude of the change is small, we proved that the change-point estimator in the non-rescaled time converges in distribution to the location of the maxima of a two-sided random walk, for which a closed-form approximation distribution is derived. The proposed method and its asymptotic properties were shown to be applicable for the INARCH process with Poisson or negative binomial conditional distributions. The finite sample performance of the proposed estimation procedure was verified in simulation studies. We analyzed the robbery data of two neighborhoods of Baltimore City from January 1, 2012 to January 5, 2019, and a change-point was identified.