MODELING NONNORMAL 1ST-ORDER AUTOREGRESSIVE TIME-SERIES

We shall first review some non-normal stationary first-order autoregre ssive models. The models are constructed with a given marginal distrib ution (logistic, hyperbolic secant, exponential, Laplace, or gamma) an d the requirement that the bivariate joint distribution of the generat ed process must be sufficiently simple so that the parameter estimatio n and forecasting problems of the models can be addressed. A model-bui lding approach that consists of model identification, estimation, diag nostic checking, and forecasting is then discussed for this class of m odels.