Abstract
This study considers the mean targeting estimation for integer-valued time series models and a parameter change test as its application. We first introduce the mean targeting quasi-maximum likelihood estimator (QMLE) based on generalized autoregressive conditional heteroscedastic (INGARCH) models and then consider the CUSUM test of (standardized) residuals. To evaluate the performance, we conduct a Monte Carlo simulation study applying a negative binomial mean targeting QMLE to Poisson INGARCH, Poisson integer-valued autoregressive (INAR), and log-linear Poisson INGARCH times series of counts, and demonstrate its validity. A real data analysis is also conducted using the drug offense data in Pittsburgh and Goldman Sachs Group stock data for illustration.
Acknowledgements
We thank the Editor, an AE, and one anonymous referee for their valuable comments.