A Parameter-Driven Logit Regression Model for Binary Time Series

• Activity Number: 310
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Rongning Wu* and Yunwei Cui
• Companies: Baruch College and University of Houston Downtown
• Keywords: Autocorrelation ; Binary time series ; Generalized linear model ; Kernel-based method ; Latent process ; Nonstationarity

Abstract

We consider a parameter-driven regression model for binary time series, where serial dependence is introduced by an autocorrelated latent process incorporated into the logit link function. Unlike in the case of parameter-driven Poisson log-linear or negative binomial logit regression model studied in the literature for time series of counts, generalized linear model (GLM) estimation of the regression coefficient vector, which suppresses the latent process and maximizes the corresponding pseudo-likelihood, cannot produce a consistent estimator. As a remedial measure, in this paper we propose a modified GLM estimation procedure, and show that the resulting estimator is consistent and asymptotically normal. Moreover, we develop two procedures for estimating the asymptotic covariance matrix of the estimator and establish their consistency property. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedures. An empirical example is also presented.