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Improved errors-in-variables estimators for grouped data

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  • Paul J. Devereux

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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    File URL: http://hdl.handle.net/10197/748
    File Function: First version, 2006
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    References listed on IDEAS

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    1. Richard Blundell & Alan Duncan & Costas Meghir, 1998. "Estimating Labor Supply Responses Using Tax Reforms," Econometrica, Econometric Society, vol. 66(4), pages 827-862, July.
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    3. Blomquist, Soren & Dahlberg, Matz, 1999. "Small Sample Properties of LIML and Jackknife IV Estimators: Experiments with Weak Instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 69-88, Jan.-Feb..
    4. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," American Economic Review, American Economic Association, vol. 80(3), pages 313-336, June.
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    7. 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.
    8. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
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    Citations

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    Cited by:

    1. Robert E.B. Lucas & Lyn Squire & T. N. Srinivasan (ed.), 2010. "Global Exchange and Poverty," Books, Edward Elgar Publishing, number 13102.
    2. 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.
    3. Vitaliy Oryshchenko, 2010. "Does Foreign Ownership Matter for Enterprise Training? Empirical Evidence from Transition Countries," Chapters,in: Global Exchange and Poverty, chapter 10 Edward Elgar Publishing.
    4. Benoit Dostie, 2012. "Labour Supply and Taxes: New Estimates of the Responses of Wives to Husbands’ Wages," Cahiers de recherche 12-02, HEC Montréal, Institut d'économie appliquée.
    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. 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.
    7. 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, pages 125-161.
    8. 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.
    9. 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.

    More about this item

    Keywords

    Psuedo-panel; Small sample bias; Labor supply; Labor supply--Mathematical models; Jackknife (Statistics); Monte Carlo method;

    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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