Unobserved components can parameterize problems of endogeneity in many nonlinear models for cross-sectional and panel data. This talk provides some examples and uses gsem to estimate the parameters.

After reviewing the potential-outcome framework for estimating treatment effects from observational data, this talk discusses how to estimate the average treatment effect and the average treatment effect on the treated using the regression-adjustment estimator, the inverse-probability-weighted...

This session introduces the use of the margins command to estimate the partial effects at the mean and the mean of the partial effects. Both the Stata syntax and the underlying statistical methods will be discussed. The presentation will also include some discussion of factor variables.

After reviewing the potential-outcome framework for estimating treatment effects from observational data, this talk discusses how to estimate the average treatment effect and the average treatment effect on the treated by the regression-adjustment estimator, the inverse-probability weighted...

This session offers an introduction to spatial econometrics using some user-written Stata commands. I will discuss the estimation and interpretation of the parameters in the cross-sectional spatial-autoregressive model. Data management issues pertaining to spatial-weighting matrices used in the...

After reviewing the potential-outcome framework for estimating treatment effects from observational data, I will discuss how to estimate the average treatment effect and the average treatment effect on the treated by the regression-adjustment estimator, the inverse-probability-weighted...

Stata 11 has the new command gmm for estimating parameters by generalized method of moments (GMM). gmm can estimate the parameters of linear and nonlinear models for cross-sectional, panel, and time-series data. In this presentation, I provide an introduction to GMM and to the gmm command

Stata 11 has a new command, gmm, for estimating parameters by the generalized method of moments (GMM). gmm can estimate the parameters of linear and nonlinear models for cross-sectional, panel, and time-series data. In this presentation, I provide an introduction to GMM and to the gmm command.

In this talk, I will review dynamic panel-data analysis and how to perform it using Stata. I also cover static models with predetermined variables. For each model discussed, I review the econometrics and then show how to perform the estimation using Stata.

In this talk, I will provide a quick introduction to estimators for the parameters of spatial-autoregressive models and a quick introduction to a suite of user-written Stata commands for managing spatial data and parameter estimation.