How to do Xtabond2: An Introduction to Difference and System GMM in Stata

Abstract

This working paper by CGD research fellow David Roodman provides an introduction to a particular class of econometric techniques, dynamic panel estimators. The techniques and their implementation in Stata, a statistical software package widely used in the research community, are an important input to the careful applied research CGD advocates.

The techniques discussed are specifically designed to extract causal lessons from data on a large number of individuals (whether countries, firms or people) each of which is observed only a few times, such as annually over five or ten years. These techniques were developed in the 1990s by authors such as Manuel Arellano, Richard Blundell and Olympia Bover, and have been widely applied to estimate everything from the impact of foreign aid to the importance of financial sector development to the effects of AIDS deaths on households.

The present paper contributes to this literature pedagogically, by providing an original synthesis and exposition of the literature on these dynamic panel estimators, and practically, by presenting the first implementation of some of these techniques in Stata. Stata is designed to encourage users to develop new commands for it, which other users can then use or even modify. In this paper Roodman introduces abar and xtabond2, which is now one of the most frequently downloaded user-written Stata commands in the world. Stata's partially open-source architecture has encouraged the growth of a vibrant world-wide community of researchers, which benefits not only from improvements made to Stata by the parent corporation, but also from the voluntary contributions of other users. Stata is arguably one of the best examples of a combination of private for-profit incentives and voluntary open-source incentives in the joint creation of a global public good.

Keywords: dynamic panel estimation, difference GMM, system GMM, Stata, Arellano-Bond, Blundell-Bond, generalized method of moments, autocorrelation

JEL Classification: C23, C87