I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

Despite the fact that many aggregates are nonlinear functions and the aggregation weights of many macroeconomic aggregates are time-varying, much of the literature on forecasting aggregates considers the case of linear aggregates with fixed, time-invariant aggregation weights. In this study a...

This paper studies some temporal dependence properties and addresses the issue of parametric estimation for a class of state-dependent autoregressive models in which we assume a stochastic autoregressive coefficient depending on the first lagged value of the process itself. We call such a model...