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Moment Estimation With Attrition: An Application to Economic Models

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  • Abowd J.M.
  • Crepon B.
  • Kramarz F.

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  • Abowd J.M. & Crepon B. & Kramarz F., 2001. "Moment Estimation With Attrition: An Application to Economic Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1223-1231, December.
    • Handle: RePEc:bes:jnlasa:v:96:y:2001:m:december:p:1223-1231
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    Cited by:

    1. Moser, Christoph & Urban, Dieter & di Mauro, Beatrice Weder, 2010. "International competitiveness, job creation and job destruction--An establishment-level study of German job flows," Journal of International Economics, Elsevier, vol. 80(2), pages 302-317, March.
    2. Michela Bia & Martin Huber & Luk'av{s} Laff'ers, 2020. "Double machine learning for sample selection models," Papers 2012.00745, arXiv.org, revised Jul 2021.
    3. Tahir Andrabi & Jishnu Das & Asim Ijaz Khwaja & Tristan Zajonc, 2011. "Do Value-Added Estimates Add Value? Accounting for Learning Dynamics," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 29-54, July.
    4. Hans Fricke & Markus Frölich & Martin Huber & Michael Lechner, 2020. "Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 481-504, August.
    5. Atalay, Kadir & Barrett, Garry F. & Staneva, Anita, 2019. "The effect of retirement on elderly cognitive functioning," Journal of Health Economics, Elsevier, vol. 66(C), pages 37-53.
    6. Martin Huber & Anna Solovyeva, 2020. "Direct and Indirect Effects under Sample Selection and Outcome Attrition," Econometrics, MDPI, vol. 8(4), pages 1-25, December.
    7. Nicoletti, Cheti, 2006. "Nonresponse in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 461-489, June.
    8. Addison, John T. & Portugal, Pedro & Varejão, José, 2014. "Labor demand research: Toward a better match between better theory and better data," Labour Economics, Elsevier, vol. 30(C), pages 4-11.
    9. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    10. Abowd, John M. & Haltiwanger, John C. & Lane, Julia & McKinney, Kevin Lee & Sandusky, L. Kristin, 2007. "Technology and the Demand for Skill: An Analysis of Within and Between Firm Differences," IZA Discussion Papers 2707, Institute of Labor Economics (IZA).
    11. Denis Heng Yan Leung & Ken Yamada & Biao Zhang, 2015. "Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 155-179, March.
    12. Joachim Inkmann, 2010. "Estimating Firm Size Elasticities of Product and Process R&D," Economica, London School of Economics and Political Science, vol. 77(306), pages 384-402, April.
    13. Takeshima, Hiroyuki, 2015. "Drivers of growth in agricultural returns to scale: The hiring in of tractor services in the Terai of Nepal:," IFPRI discussion papers 1476, International Food Policy Research Institute (IFPRI).
    14. Anton Flossmann, 2010. "Accounting for missing data in M-estimation: a general matching approach," Empirical Economics, Springer, vol. 38(1), pages 85-117, February.
    15. Inkmann, J., 2005. "Inverse Probability Weighted Generalised Empirical Likelihood Estimators : Firm Size and R&D Revisited," Other publications TiSEM c39cff1f-16c1-4446-a83f-c, Tilburg University, School of Economics and Management.

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