A1416
Title: Detecting multiple changes in linear models with heteroscedastic errors
Authors: Yuqian Zhao - University of Sussex (United Kingdom) [presenting]
Abstract: The problem of detecting change points in the regression parameters of a standard linear regression model is considered. Asymptotic results of the weighted functionals of the cumulative sum (CUSUM) process of the residuals are established to the model errors and/or covariates exhibit heteroscedasticity, and these theoretical results illuminate how to adapt standard change point test statistics to this situation. Such adaptations are studied in a simulation study along with a method based on a classical Vostrikova approximation to improve the finite sample performance of these tests, which shows that they work well in practice to detect multiple change points in the linear model parameters and control the Type-I testing error in the presence of heteroscedasticity. The proposed methods are illustrated with applications for testing the instability of predictive regression models and changes in investor sentiment in the U.S. stock market.
