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...

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 proposing a simple theoretical framework to illustrate the importance of third-country effects in empirical studies of the Pollution Haven Hypothesis, we test the model using state-level panel data on inbound US FDI and relative abatement costs. Our analysis reveals that while own state...

I research the market efficiency of the German 6/49 parimutuel lottery game using Stata. To this end, I investigate the existence of profit opportunities for particularly unpopular combinations of numbers (Papachristou and Karamanis (1998)), employing the covariates proposed by Henze and Riedwyl...

A Stata program will be presented for improved quality control of econometric models. It is well known that reported econometric results often have unknown reliability because of selective reporting by the researcher. In particular, t-statistics are often uninformative or misleading when...

Literature on causal inference has emphasized the average causal effect, defined as the mean difference in potential outcomes under different treatment conditions. We consider marginal regression models that describe how causal effects vary in relation to covariates. To estimate parameters, we...