scispace - formally typeset
Search or ask a question
Author

Mark E. Schaffer

Bio: Mark E. Schaffer is an academic researcher from Heriot-Watt University. The author has contributed to research in topics: Instrumental variable & Test statistic. The author has an hindex of 41, co-authored 152 publications receiving 8586 citations. Previous affiliations of Mark E. Schaffer include Center for Economic and Policy Research & Institute for the Study of Labor.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the authors 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.
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.

2,444 citations

Journal ArticleDOI
TL;DR: In this paper, the authors extend their 2003 paper on instrumental variables and generalized method of moments estimation, and test and describe enhanced routines that address heteroskedasticity- and autocorrelation-consistency.
Abstract: We extend our 2003 paper on instrumental variables and generalized method of moments estimation, and we test and describe enhanced routines that address heteroskedasticity- and autocorrelation-cons...

928 citations

Posted Content
TL;DR: In this paper, the authors extend their 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates.
Abstract: We extend our 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates.

649 citations

Posted Content
TL;DR: xtivreg2 as mentioned in this paper is a wrapper for ivreg28, which can be installed on Stata versions 9+ and can be used to estimate fixed-effects and first-difference panel data models with possibly endogenous regressors.
Abstract: xtivreg28 implements IV/GMM estimation of the fixed-effects and first-differences panel data models with possibly endogenous regressors. It is essentially a wrapper for ivreg28, which must be installed for xtivreg28 to run. Users of Stata versions 9+ should use xtivreg2. xtivreg28 supports all the estimation and reporting options of ivreg28; see help ivreg28 for full descriptions and examples. In particular, all the statistics available with ivreg28 (heteroskedastic, cluster- and autocorrelation-robust covariance matrix and standard errors, overidentification and orthogonality tests, first-stage and weak/underidentification statistics, etc.) are also supported by xtivreg2 and will be reported with any degrees-of-freedom adjustments required for a panel data estimation.

491 citations

Posted Content
TL;DR: For example, overid computes versions of a test of overidentifying restrictions (orthogonality conditions) for a panel data estimation as mentioned in this paper, where the test statistic is distributed as chi-squared with degrees of freedom = L-K, where L is the number of excluded instruments and K is the total number of regressors.
Abstract: xtoverid computes versions of a test of overidentifying restrictions (orthogonality conditions) for a panel data estimation. For an instrumental variables estimation, this is a test of the null hypothesis that the excluded instruments are valid instruments, i.e., uncorrelated with the error term and correctly excluded from the estimated equation. The test statistic is distributed as chi-squared with degrees of freedom = L-K, where L is the number of excluded instruments and K is the number of regressors, and a rejection casts doubt on the validity of the instruments. xtoverid will report tests of overidentifying restrictions after IV estimation using fixed effects, first differences, random effects, and the Hausman-Taylor estimator. A test of fixed vs. random effects is also a test of overidentifying restrictions, and xtoverid will report this test after a standard panel data estimation with xtreg,re. This routine is now included in the overid package.

253 citations


Cited by
More filters
Book
01 Jan 2009

8,216 citations

Journal ArticleDOI
TL;DR: This pedagogic paper first introduces linear GMM, and 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.
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.

5,458 citations

Journal ArticleDOI
TL;DR: This paper introduced linear generalized method of moments (GMM) estimators for situations with small T, large N panels, 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.
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.

5,416 citations

Posted Content
TL;DR: A theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification.
Abstract: Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

4,284 citations

01 Jan 2002
TL;DR: This article investigated whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997) with negative results.
Abstract: We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

3,770 citations