Autoregressive Models With Stochastic Design Variables and Nonnormal Innovations

Abstract

In autoregression models the design variable has traditionally been assumed to be non-stochastic and innovations are normal. In most real life situations, however, the design variable is stochastic having a non-normal distribution as the innovations. Modified maximum likelihood method is utilized to estimate unknown parameters in such situations. Closed form estimators are obtained and shown to be efficient and robust.