Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence
Robust Change Point Test for General Integer-Valued Time Series Models Based on Density Power Divergence
Abstract
In this study, we consider the problem of testing for a parameter change in general integer-valued time series models whose conditional distribution belongs to the one-parameter exponential family when the data are contaminated by outliers. In particular, we use a robust change point test based on density power divergence (DPD) as the objective function of the minimum density power divergence estimator (MDPDE). The results show that under regularity conditions, the limiting null distribution of the DPD-based test is a function of a Brownian bridge. Monte Carlo simulations are conducted to evaluate the performance of the proposed test and show that the test inherits the robust properties of the MDPDE and DPD. Lastly, we demonstrate the proposed test using a real data analysis of the return times of extreme events related to Goldman Sachs Group stock. View Full-Text
Keywords: integer-valued time series; one-parameter exponential family; minimum density power divergence estimator; density power divergence; robust change point test
Full Paper can be downloaded at: https://www.mdpi.com/1099-4300/22/4/493
This article belongs to the Special Issue Robust Procedures for Estimating and Testing in the Framework of Divergence Measures
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