A new binomial autoregressive process with explanatory variables

In this paper we propose a new class of random coefficients binomial autoregressive models. With logit transformation, the explanatory variables are incorporated into the coefficients. The model can capture the smooth changes and the influence of the environmental factors to the coefficients. The conditional least squares (CLS) and conditional maximum likelihood (CML) estimators are considered and the related asymptotic properties are derived. We also investigate the finite-sample performance of the proposed methods in simulation studies. Finally, we apply the model to a real data set, which shows that the new model does rather well.