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

Roodman, David

doi:10.1177/1536867x0900900106

Cited by 9,848Spatial and Panel Data AnalysisAdvanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

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

Roodman, David·2009·The Stata Journal: Promoting communications on statistics and Stata

DOI 10.1177/1536867x0900900106

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Abstract

The difference and system generalized method-of-moments estimators, developed by Holtz-Eakin, Newey, and Rosen (1988, Econometrica 56: 1371–1395); Arellano and Bond (1991, Review of Economic Studies 58: 277–297); Arellano and Bover (1995, Journal of Econometrics 68: 29–51); and Blundell and Bond (1998, Journal of Econometrics 87: 115–143), are increasingly popular. Both are general estimators designed for situations with “small T, large N″ panels, meaning few time periods and many individuals; independent variables that are not strictly exogenous, meaning they are correlated with past and possibly current realizations of the error; fixed effects; and heteroskedasticity and autocorrelation within individuals. This pedagogic article first introduces linear generalized method of moments. Then it describes how limited time span and potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way. Next it describes 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 article concludes with some tips for proper use.

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The dataset structure ensures sufficient cross-sectional and temporal variation for reliable instrument construction and valid inference.
“this structure ensures sufficient cross-sectional and temporal variation for reliable instrument construction and valid inference, in line with roodman (2009).”
From Remittance–economic growth linkage in Africa: the roles of financial development and inflation (2025) · cites this paper as roodman (2009)
As per Roodman [63] and Ali et al. [68], AR(1) should be less than 0.05 and AR(2) should be more than 0.05 for results accuracy.
“As per Roodman [63] and Ali et al. [68], AR (1) should be less than 0.05%, and AR (2) should be more than 0.05% significant level for results accuracy, model reliability, and overcoming the insignificant effects of AR (1).”
From Do institutional governance and state fragility affect institutional quality in Asian economies? (2023) · cites this paper as [63]
There is no second-order autocorrelation in the residuals.
“Secondly, we verify the absence of second-order autocorrelation ( AR2 ) in the residuals (Roodman 2009).”
From Innovative development financing amidst uncertainty: how can African countries leverage domestic resource mobilization? (2025) · cites this paper as Roodman 2009

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Authors: Roodman, D.
Affiliations: Center for Global Development (US)
Corresponding author: Roodman, David
ORCID: Roodman, David
Venue: The Stata Journal: Promoting communications on statistics and Stata
Publisher: SAGE Publications
Published: 2009-03
Volume: 9
Issue: 1
Pages: 86-136
ISSN: 1536-867X (ISSN-L)
1536-8734
DOI: 10.1177/1536867x0900900106
OpenAlex: W3123200913
Cited by: 9,848
References: 25
Citations by year: 2026: 1113 · 2025: 1016 · 2024: 907 · 2023: 960 · 2022: 860 · 2021: 812 · 2020: 692 · 2019: 511 · 2018: 441 · 2017: 387 · 2016: 382 · 2015: 362 · 2014: 368 · 2013: 279 · 2012: 255
FWCI: 507.21
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: The Stata Journal Promoting communications on statistics and Stata (publishedVersion)
Topics: Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: system GMM, generalized method of moments, panel data, endogenous regressors, fixed effects, Stata

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

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