The Stata package “multilevel tools” (mlt) includes a range of ado-files for postestimation after multilevel models (xtmixed/xtmelogit). Up to now, it contains three commands (more ado-files will be added in the future): mltrsq gives the Boskers/Snijders R-square and the Bryk/Raudenbusch...

Dong and Lewbel have developed the theory of simple estimators for binary choice models with endogenous or mismeasured regressors, depending on a “special regressor” as defined by Lewbel (Journal of Econometrics, 2000). These estimators can be used with limited, censored, continuous, or...

In this talk, I aim to discuss tools to compare the observed distribution of a variable with the theoretical distribution assumed by a model. In particular, I will focus on the situation where a model assumes a certain distribution for the explained/dependent/y variable and one or more...

Multilevel multiprocess models are routinely used to study parallel processes of repeated demographic events, like births, union formation, and union dissolution. Multilevel multiprocess models are simultaneous equations for hazards including heterogeneity components, and the joint estimation of...

Because of the scaling of the unobserved latent dependent variable in logistic and probit multilevel models, the lowest level residual variance is always pi^2/3 (logistic regression) or 1.0 (probit regression). As a consequence, a change of regression coefficients and variance components between...

In an era in which doctors and patients aspire to personalized medicine, detecting and modeling interactions between covariates or between covariates and treatment is becoming increasingly important. In observational studies, for example, in epidemiology, interactions are known as effect...

Visualizing the true effect of a predictor over a range of values can be difficult for models that are not parameterized in their natural metric, such as for logistic or (even more so) probit models. Interaction terms in such models cause even more fogginess. In this talk, I show how both the...

Least-squares regression is a major workhorse in applied research. Yet its estimates may be deemed nonrobust under various conditions. One example is heavy-tailed error distributions, in which least-squares estimation may lose its cutting edge with respect to efficiency. More importantly,...

In this talk, I will present the basic principles of exploratory spatial data analysis and their application using Stata. After a brief discussion of the specific features of spatial data, I will show some freely-available user-written Stata commands (spmap, spgrid, spkde, spatwmat, spatgsa,...

Stata provides an easy and effective way of programming and distributing user-written additions to Stata’s command universe. However, a Stata programmer may face problems when trying to distribute an ado-file whose code in turn depends on one or many other self-written or third-party...