In recent years, much interest has focused on network analysis. This study presents and applies to real data a new user-written Stata command called datanet, which facilitates the dataset organization for the purpose of network analysis. Given a fixed number of units (or nodes) belonging to the...

The margins command in Stata allows us to get a wide array of results using coefficient estimates. I will illustrate the use of margins in some commonly used models. I will then illustrate a new result. I will show how we can use margins after fixed-effects panel-data estimation that...

The purpose of this presentation is to present a step-by-step tutorial on how to draw thematic maps of Italy using the Stata user-written command spmap and spatially referenced data freely available on the Internet.

This talk introduces the principles of propensity-score theory and reviews available programs to implement propensity-score methods in Stata, with particular focus on psmatch2 and teffects psmatch. An application on real data will be shown.

The classical stochastic frontier panel-data models provide no mechanism to disentangle individual time-invariant unobserved heterogeneity from inefficiency. Greene (2005a,b) proposed the so-called true fixed-effects specification that distinguishes these two latent components and allows for...

This presentation presents a parametric counterfactual model identifying average treatment effects (ATEs) by conditional mean independence when externality (or neighborhood) effects are incorporated within the traditional Rubin potential-outcome model. As such, it tries to generalize the usual...

We extend the univariate results in Wooldridge (2005) to multivariate probit models, proving the following. 1) Average partial effects (APEs) based on joint probabilities are consistently estimated by conventional multivariate probit models under general forms of conditionally independent latent...

Data augmentation is a technique for conducting approximate Bayesian regression analysis. This technique is a form of penalized likelihood estimation where prior information, represented by one or more specific prior data records, generates a penalty function that imposes the desired priors on...

This presentation will manage the description of methods of handling large datasets in Stata effectively and efficiently. The course will be based on general considerations of issues related to the management of large datasets on practical examples of specific datasets. We will cover the...

Writing a document that contains statistical results in its narrative, including inline results, can take too much effort. Typically, users have a separate series of do-files whose results must then be pulled into the document. Reproducible research greatly lessens document-maintenance chores by...