Limit Theory for Random Coefficient First-Order Autoregressive Process

Random coefficient AR(1) processes are investigated where the random coefficient satisfies some suitable conditions. Conditional least squares estimator is shown to be consistent and to be asymptotically normality distributed. This extends the limit theory for stationary and near-stationary cases. Only second moments are assumed, as in the case of stationary autoregression models with fixed coefficient |ρ| < 1.