Measurement error in exposure variables can lead to bias in effect estimates, and methods that aim to correct this bias often come at the price of greater standard errors (and so, lower statistical power). This means that standard sample size calculations are inadequate and that, in general,...

Multiple imputation (MI) is known as an effective method for handling missing data. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a variable. This study examines the effectiveness of MI in data with 10% to 80%...

Econometricians have begun to devote more attention to spatial interactions when carrying out applied econometric studies. In part, this is motivated by an explicit focus on spatial interactions in policy formulation or market behavior, but it may also reflect concern about the role of omitted...

Users often need a consistent look for their Stata graphs for publications or internal documents. To change one graph, the user can include options. To change many graphs at once, it is better to create a scheme or graph recording, which automates the changes and simplifies the graph commands. I...

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...

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,...

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...