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Improved Errors-in-Variables Estimators for Grouped Data

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  • Paul J Devereux

    (University College of Dublin)

Abstract

Grouping models are widely used in economics but are subject to finite sample bias. I show that the standard errors-in-variables estimator (EVE) is exactly equivalent to the Jackknife Instrumental Variables Estimator (JIVE), and use this relationship to develop an estimator which, unlike EVE, is unbiased in finite samples. The theoretical results are demonstrated using Monte Carlo experiments. Finally, I implement a model of intertemporal male labor supply using microdata from the United States Census. There are sizeable differences in the wage elasticity across estimators, showing the practical importance of the theoretical issues even when the sample size is quite large.

Suggested Citation

  • Paul J Devereux, 2006. "Improved Errors-in-Variables Estimators for Grouped Data," Working Papers 200602, School of Economics, University College Dublin.
  • Handle: RePEc:ucn:wpaper:200602
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    Cited by:

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    2. Abe, Yukiko & Tamada, Keiko, 2010. "Regional patterns of employment changes of less-educated men in Japan: 1990-2007," Japan and the World Economy, Elsevier, vol. 22(2), pages 69-79, March.
    3. Klára Kalíšková, 2020. "Tax and transfer policies and the female labor supply in the EU," Empirical Economics, Springer, vol. 58(2), pages 749-775, February.
    4. Francisca Antman & David J. McKenzie, 2007. "Earnings Mobility and Measurement Error: A Pseudo-Panel Approach," Economic Development and Cultural Change, University of Chicago Press, vol. 56(1), pages 125-161, October.
    5. Paul J. Devereux, 2007. "Small-sample bias in synthetic cohort models of labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 839-848.
    6. Daniel A. Ackerberg & Paul J. Devereux, 2009. "Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 91(2), pages 351-362, May.
    7. John V. Winters, 2020. "In-State College Enrollment and Later Life Location Decisions," Journal of Human Resources, University of Wisconsin Press, vol. 55(4), pages 1400-1426.
    8. Dwayne Benjamin & Loren Brandt & John Giles, 2011. "Did Higher Inequality Impede Growth in Rural China?," Economic Journal, Royal Economic Society, vol. 121(557), pages 1281-1309, December.
    9. Rumman Khan, 2021. "Assessing Sampling Error in Pseudo‐Panel Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 742-769, June.
    10. Robert E.B. Lucas & Lyn Squire & T. N. Srinivasan (ed.), 2010. "Global Exchange and Poverty," Books, Edward Elgar Publishing, number 13102, December.
    11. Kasey S. Buckles & Daniel M. Hungerman, 2013. "Season of Birth and Later Outcomes: Old Questions, New Answers," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 711-724, July.
    12. Vitaliy Oryshchenko, 2010. "Does Foreign Ownership Matter for Enterprise Training? Empirical Evidence from Transition Countries," Chapters, in: Robert E.B. Lucas & Lyn Squire & T. N. Srinivasan (ed.), Global Exchange and Poverty, chapter 10, Edward Elgar Publishing.
    13. Allan Seuri, 2019. "Estimating tax income elasticities using a group-averaged synthetic tax instrument," Economics Bulletin, AccessEcon, vol. 39(3), pages 2137-2141.
    14. Hou, Feng & Lu, Yuqian & Morissette, René, 2009. "Marriage, Cohabitation and Women’s Response to Changes in the Male Wage Structure," CLSSRN working papers clsrn_admin-2009-45, Vancouver School of Economics, revised 30 Aug 2009.
    15. René Morissette & Feng Hou, 2008. "Does the labour supply of wives respond to husbands' wages? Canadian evidence from micro data and grouped data," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(4), pages 1185-1210, November.

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    More about this item

    Keywords

    psuedo-panel; small sample bias; labor supply;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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