Random Coefficient Autoregressive Processes:a Markov Chain Analysis of Stationarity and Finiteness of Moments

Simple yet practically efficient conditions for the ergodicity of a Markov chain on a general state space have recently been developed. We illustrate their application to non‐linear time series models and, in particular, to random coefficient autoregressive models. As well as ensuring the existence of a unique stationary distribution, geometric rates of convergence to stationarity are ensured. Moreover, sufficient conditions for the existence and convergence of moments can be determined by a closely related method. The latter conditions, in particular, are new.