Flexible binomial AR(1) processes using copulas

Abstract

In order to accurately and flexibly capture the correlation structure between two random coefficients in the binomial AR(1) process, we propose a new class of models with copula. We derive some basic properties of the process. Then we discuss the conditional least squares (CLS) and conditional maximum likelihood (CML) estimators, as well as their asymptotic properties. We also investigate the finite-sample performance of the proposed method in simulation studies. Finally, a real data example is provided to illustrate the model.