In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is...

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed "initial conditions problem," as well as the...

We study the information content of the University of Michigan's Index of Consumer Sentiment as well as its five components. Using household data from the Surveys of Consumers, we identify the main determinants of these indicators and document their varying role over the business cycle. Our...

In this paper we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an assumptionfree nonparametric density specification while other alternative-specific coefficients are assumed to...

I estimate a forward-looking, dynamic, discrete-choice monetary policy reaction function for the US economy, that accounts for the fact that there are substantial restrictions in the period-to-period changes of the Fed's policy instrument. I find a substantial contrast between the periods before...