We emphasize using our solutions to the problems of omitted variables, measurement errors, and unknown functional forms to improve model specification, and to estimate the mean square error of an empirical best linear unbiased predictor of an individual drawing of the dependent variable of an...

This paper develops a general method for conducting exact small-sample inference in models which allow the estimator of the (scalar) parameter of interest to be expressed as the root of an estimating function, and which is particularly simple to implement for linear models with a covariance...

Advances in computing power allow the empirical researcher to use intensive computional techniques to solve and estimate nonlinear panel-data models, specifically those arising from nonlinear panel data such as Probit and Tobit models. In these cases, maximum-likelihood estimation can be...

I examine the impact of demand uncertainty on a firm's investment decisions. Recent theoretical work accounts for the degree of irreversibility of capital investment. Dixit and Pindyck deliver a clear prediction: an increase in uncertainty lowers current investment. With irreversible investment,...

Computing power now allows empirical researchers to use intensive computing estimation techniques with nonlinear panel-data models. Maximum Likelihood estimation is often cumbersome, if not analytically intractable, when dealing with such models. Even the simple calculation of the likelihood...