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Panel Data Estimators for Nonseparable Models with Endogenous Regressors

  • Joseph G. Altonji
  • Rosa L. Matzkin

We propose two new estimators for a wide class of panel data models with nonseparable error terms and endogenous explanatory variables. The first estimator covers qualitative choice models and both estimators cover models with continuous dependent variables. The first estimator requires the existence of a vector z such that the density of the error term does not depend on the explanatory variables once one conditions on z. In some panel data cases we may find z by making the assumption that the distribution of the error term conditional on the vector of the explanatory variables for each cross-section' unit in the panel is exchangeable in the values of those explanatory variables. This situation may be realistic, in particular, when each unit is a group of individuals, so that the observations are across groups and for different individuals in each group. The basic idea is to first estimate the slope of the mean of the dependent variable conditional on both the explanatory variable and z and then undo the effect of conditioning on z by taking the average of the slope over the distribution of z conditional on a particular value of the explanatory variable. We also extend the procedure to the case in which the explanatory variable is endogenous conditional on z but an instrumental variable is available. The second estimator is based on the assumption that the error distribution is exchangeable in the explanatory variables of each unit. It applies to models that are monotone in the error term. A shift in the value of an explanatory variable for member 1 of a group has both a direct effect on the distribution of the dependent variable for member 1 and an indirect effect through the distribution of the error. A shift in the explanatory variable has an indirect effect on the dependent variable for other members of the panel but no direct effect. We isolate the direct effect by comparing the effect of the explanatory variable on the distribution of the dependent variable for member 1 to its effect on the distribution for the other panel members.

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File URL: http://www.nber.org/papers/t0267.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0267.

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Date of creation: Mar 2001
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Publication status: published as Altonji, Joseph G. and Rosa L. Matzkin. "Cross Section And Panel Data Estimators For Nonseparable Models With Endogenous Regressors," Econometrica, v73(4,Jul), 2005, 1053-1101.
Handle: RePEc:nbr:nberte:0267
Note: TWP
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  1. Daniel Aaronson, 1998. "Using Sibling Data to Estimate the Impact of Neighborhoods on Children's Educational Outcomes," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 915-946.
  2. Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2012. "Estimating Derivatives in Nonseparable Models With Limited Dependent Variables," Econometrica, Econometric Society, vol. 80(4), pages 1701-1719, 07.
  3. Currie, J. & Thomas, D., 1995. "Does Head Start make a Difference?," Papers 95-10, RAND - Reprint Series.
  4. Joseph G. Altonji & Fumio Hayashi & Laurence Kotlikoff, . "The Effects of Income and Wealth on Time and MOney Transfers Between Parents and Children," IPR working papers 96-5, Institute for Policy Resarch at Northwestern University.
  5. Rosa L. Matzkin, 1999. "Nonparametric Estimation of Nonadditive Random Functions," Working Papers 38, Universidad de San Andres, Departamento de Economia, revised Sep 2001.
  6. Rivers, Douglas & Vuong, Quang H., 1988. "Limited information estimators and exogeneity tests for simultaneous probit models," Journal of Econometrics, Elsevier, vol. 39(3), pages 347-366, November.
  7. Janet Currie & Bruce Fallick, 1993. "The Minimum Wage and the Employment of Youth: Evidence from the NLSY," NBER Working Papers 4348, National Bureau of Economic Research, Inc.
  8. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  9. James Heckman & Edward Vytlacil, 1998. "Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return is Correlated with Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 33(4), pages 974-987.
  10. Honore, Bo E, 1992. "Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects," Econometrica, Econometric Society, vol. 60(3), pages 533-65, May.
  11. Donald J. Brown & Rosa L. Matzkin, 1998. "Estimation of Nonparametric Functions in Simultaneous Equations Models, with an Application to Consumer Demand," Cowles Foundation Discussion Papers 1175, Cowles Foundation for Research in Economics, Yale University.
  12. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
  13. Geronimus, Arline T & Korenman, Sanders, 1992. "The Socioeconomic Consequences of Teen Childbearing Reconsidered," The Quarterly Journal of Economics, MIT Press, vol. 107(4), pages 1187-214, November.
  14. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-31, May.
  15. Abrevaya, Jason, 2000. "Rank estimation of a generalized fixed-effects regression model," Journal of Econometrics, Elsevier, vol. 95(1), pages 1-23, March.
  16. Mark R. Rosenzweig & Kenneth I. Wolpin, 1994. "Inequality among Young Adult Siblings, Public Assistance Programs, and Intergenerational Living Arrangements," Journal of Human Resources, University of Wisconsin Press, vol. 29(4), pages 1101-1125.
  17. Chamberlain, Gary, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 225-38, January.
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