Stata Journal 

About: Stata Journal is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Computer science & Estimator. It has an ISSN identifier of 1536-867X. Over the lifetime, 1054 publications have been published receiving 85622 citations.

How to do xtabond2: An introduction to difference and system GMM in Stata

Abstract: The Arellano-Bond (1991) and Arellano-Bover (1995)/Blundell-Bond (1998) linear generalized method of moments (GMM) estimators are increasingly popular. Both are general estimators designed for situations with “small T, large N” panels, meaning few time periods and many individuals; with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals. This pedagogic paper first introduces linear GMM. Then it shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it shows how to apply these estimators with xtabond2. It also explains how to perform the Arellano-Bond test for autocorrelation in a panel after other Stata commands, using abar. The Center for Global Development is an independent think tank that works to reduce global poverty and inequality through rigorous research and active engagement with the policy community. Use and dissemination of this Working Paper is encouraged, however reproduced copies may not be used for commercial purposes. Further usage is permitted under the terms of the Creative Commons License. The views expressed in this paper are those of the author and should not be attributed to the directors or funders of the Center for Global Development.

Estimation of average treatment effects based on propensity scores

Abstract: In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature, and we provide a set of Stata programs, which we illustrate using the Na...

Instrumental variables and GMM: Estimation and testing

Abstract: We discuss instrumental variables (IV) estimation in the broader con- text of the generalized method of moments (GMM), and describe an extended IV estimation routine that provides GMM estimates as well as additional diagnostic tests. Stand{alone test procedures for heteroskedasticity, overidentication, and endogeneity in the IV context are also described.

Multiple Imputation of Missing Values

Abstract: Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of "complete-case" analysis of datasets with missing observations. In medicine, for example, observa- tions may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained equations. The basic idea of data analysis with multiple imputation is to create a small number (e.g., 5-10) of copies of the data, each of which has the missing values suitably imputed, and analyze each complete dataset independently. Estimates of parameters of inter- est are averaged across the copies to give a single estimate. Standard errors are computed according to the "Rubin rules", devised to allow for the between- and within-imputation components of variation in the parameter estimates. This arti- cle describes five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a mul- tiply imputed dataset, combining the estimates using Rubin's rules, and supports survival analysis models (stcox and streg), categorical data models, generalized linear models, and more. Finally, misplit and mijoin are utilities to intercon- vert datasets created by mvis and by the miset program from John Carlin and colleagues. The use of the routines is illustrated with an example of prognostic modeling in breast cancer.

Robust standard errors for panel regressions with cross–sectional dependence

Abstract: I present a new Stata program, xtscc, that estimates pooled or- dinary least-squares/weighted least-squares regression and xed-eects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549{560) standard errors. By running Monte Carlo simulations, I compare the nite-sample properties of the cross-sectional dependence{consistent Driscoll{ Kraay estimator with the properties of other, more commonly used covariance ma- trix estimators that do not account for cross-sectional dependence. The results in- dicate that Driscoll{Kraay standard errors are well calibrated when cross-sectional dependence is present. However, erroneously ignoring cross-sectional correlation in the estimation of panel models can lead to severely biased statistical results. I illustrate the xtscc program by considering an application from empirical nance. Thereby, I also propose a Hausman-type test for xed eects that is robust to general forms of cross-sectional and temporal dependence.