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Identification with excess heterogeneity

  • Andrew Chesher

    ()

    (Institute for Fiscal Studies and University College London)

An outcome is determined by a structural function in which the effect of variables of interest is transmitted through a scalar function of those variables - an index. Multiple sources of stochastic variation are permitted to appear as arguments of the structural function, but not as arguments of the index. Conditions are provided under which there is local identification of ratios of partial derivatives of the index.

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File URL: http://cemmap.ifs.org.uk/wps/cwp1905.pdf
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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP19/05.

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Date of creation: Dec 2005
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Handle: RePEc:ifs:cemmap:19/05
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  1. Chesher, Andrew & Santos Silva, J M C, 2002. "Taste Variation in Discrete Choice Models," Review of Economic Studies, Wiley Blackwell, vol. 69(1), pages 147-68, January.
  2. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt1j6814b3, University of California Transportation Center.
  3. Han, Aaron K., 1987. "A non-parametric analysis of transformations," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 191-209, July.
  4. Barry R. Chiswick, 1974. "Income Inequality: Regional Analyses within a Human Capital Framework," NBER Books, National Bureau of Economic Research, Inc, number chis74-1, December.
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  7. Richard W. Blundell & James L. Powell, 2004. "Endogeneity in Semiparametric Binary Response Models," Review of Economic Studies, Wiley Blackwell, vol. 71, pages 655-679, 07.
  8. Elie Tamer, 2000. "Inference in Censored Models with Endogenous Regressors," Econometric Society World Congress 2000 Contributed Papers 1815, Econometric Society.
  9. Van den Berg, Gerard J., 2001. "Duration models: specification, identification and multiple durations," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 55, pages 3381-3460 Elsevier.
  10. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
  11. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-30, November.
  12. Khan, Shakeeb, 2001. "Two-stage rank estimation of quantile index models," Journal of Econometrics, Elsevier, vol. 100(2), pages 319-355, February.
  13. Honore, Bo E. & Hu, Luojia, 2004. "Estimation of cross sectional and panel data censored regression models with endogeneity," Journal of Econometrics, Elsevier, vol. 122(2), pages 293-316, October.
  14. Arthur Lewbel, 1999. "Semiparametric Qualitative Response Model Estimation with Unknown Heteroskedasticity or Instrumental Variables," Boston College Working Papers in Economics 454, Boston College Department of Economics.
  15. Newey, Whitney K & Stoker, Thomas M, 1993. "Efficiency of Weighted Average Derivative Estimators and Index Models," Econometrica, Econometric Society, vol. 61(5), pages 1199-223, September.
  16. Andrew Chesher, 2002. "Semiparametric identification in duration models," CeMMAP working papers CWP20/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  17. Chiswick, Barry R & Mincer, Jacob, 1972. "Time-Series Changes in Personal Income Inequality in the United States from 1939, with Projections to 1985," Journal of Political Economy, University of Chicago Press, vol. 80(3), pages S34-S66, Part II, .
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