A brief survey of clustered errors, focusing on estimating cluster–robust standard errors: when and why to use the cluster option (nearly always in panel regressions), and implications. Additional topics may include using svyset to specify clustering, multidimensional clustering, clustering in...

Using loops and macros in Stata can hold many advantages, mainly reducing the length of your do files, allowing errors to be tracked and fixed quickly and efficiently, faster running do files and providing us with re-usable programs which can be used in subsequent data analyses with similar...

We will take a quick tour of the graph editor, covering the basic concepts: adding text, lines, and markers; changing the defaults for added objects; changing properties; working quickly by combining the contextual toolbars with the more object dialogs; and using the object browser effectively....

One of Stata’s great strengths is its data management abilities. When either building or sharing datasets, some of the most time-consuming activities are validating the data and writing documentation for the data. Much of this futility could be avoided if datasets were self-contained, i.e., if...

Describing batches of data in terms of their order statistics or quantiles has long roots, but remains underrated in graphically-based exploration, data reduction and data reporting. Hosking in 1990 proposed L-moments based on quantiles as a unifying framework for summarizing distribution...

Three different methods have been suggested in the econometrics literature to deal with the initial conditions problem in dynamic Probit models for panel data. Heckman (1981) suggest to approximate the reduced form marginal probability of the initial state with a Probit model and allow free...

An overview of using Stata to perform candidate gene association analysis will be presented. Areas covered will include data manipulation, Hardy–Weinberg equilibrium, calculating and plotting linkage disequilibrium, estimating haplotypes, and interfacing with external programs.

A new command metamiss performs meta-analysis when some or all studies have missing data. A variety of assumptions are available, including missing-at-random, missing=failure, worst and best cases, and incorporating a user-specified prior distribution for the degree of informative missingness....

This talk discusses ineqrbd, a program for OLS regression-based decomposition suggested by G.S. Fields (“Accounting for Income Inequality and Its Change: A New Method, with Application to the Distribution of Earnings in the United States”, Research in Labor Economics, 2003). It provides an...

WinBUGS is a program for Bayesian model fitting by Gibbs sampling. WinBUGS has limited facilities for data handling, whereas Stata has excellent data handling but no routines for Bayesian analysis; therefore, much can be gained by running Stata and WinBUGS together. This talk explains the use of...