GMM estimation of the covariance structure of longitudinal data on earnings
AbstractIn this article, we discuss generalized method of moments estimation of the covariance structure of longitudinal data on earnings, and we introduce and illustrate a Stata program that facilitates the implementation of the generalized method of moments approach in this context. The program, gmmcovearn, estimates a variety of models that encompass those most commonly used by labor economists. These include models where the permanent component of earnings follows a random growth or random walk process and where the transitory component can follow either an AR(1) or an ARMA(1,1) process. In addition, time-factor loadings and cohort-factor loadings may be incorporated in the transitory and permanent components.
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Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 11 (2011)
Issue (Month): 3 (September)
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- Aedin Doris & Donal O'Neill & Olive Sweetman, 2010.
"Identification of the Covariance Structure of Earnings using the GMM Estimator,"
Economics, Finance and Accounting Department Working Paper Series
n208-10.pdf, Department of Economics, Finance and Accounting, National University of Ireland - Maynooth.
- Aedín Doris & Donal O’Neill & Olive Sweetman, 2013. "Identification of the covariance structure of earnings using the GMM estimator," Journal of Economic Inequality, Springer, vol. 11(3), pages 343-372, September.
- Doris, Aedin & O'Neill, Donal & Sweetman, Olive, 2010. "Identification of the Covariance Structure of Earnings Using the GMM Estimator," IZA Discussion Papers 4952, Institute for the Study of Labor (IZA).
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