Identification of the Covariance Structure of Earnings Using the GMM Estimator
AbstractIn this paper we study the performance of the GMM estimator in the context of the covariance structure of earnings. Using analytical and Monte Carlo techniques we examine the sensitivity of parameter identification to key features such as panel length, sample size, the degree of persistence of earnings shocks and the evolution of inequality over time. We show that the interaction of transitory persistence with the time pattern of inequality determines identification in these models and offer some practical recommendations that follow from our findings.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4952.
Length: 32 pages
Date of creation: May 2010
Date of revision:
Publication status: published in: Journal of Economic Inequality, 2013, 11 (3), 343-372
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Other versions of this item:
- 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.
- 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.
- J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
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