New strategies for the implementation of maximum likelihood estimation of nonlinear time series models are suggested. They make use of recent work on the EM algorithm and iterative simulation techniques. The estimation procedures are applied to the problem of fitting stochastic variance models...

This paper presents a straightforward set of Bayesian techniques for analyzing models involving limited dependent variables; the techniques are demonstrated in an analysis of Kennan's (1985) data on contract strikes in U.S. manufacturing. The data are analyzed by deriving posterior...

We extend here our earlier work (Laroque-Salanie, 1989) and propose a dynamic simulated pseudo-maximum likelihood method to deal with a very general class of dynamic non-linear models, including models with lagged latent variables. We test this method on Monte Carlo-generated data for a...

This paper provides an empirical test of the long-run implications of the production smoothing model of inventories, the dominant framework for inventory investment research in the past. Intertemporal models of a firm holding inventories of finished goods predict a long-run relationship between...

This paper examines the role of the rate of information arrival proxy variables, as they relate to persistence in the variance structure of minute-by-minute S&P 500 Index Futures returns series. The role of contract volume, floor transactions, the number of price changes, executed order...