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Inference in Limited Dependent Variable Models Robust to Weak Identification

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  • Leandro M. Magnusson

    () (Department of Economics, Tulane University)

Abstract

We propose tests for structural parameters in limited dependent variable models with endogenous explanatory variables using the classical minimum distance framework. These tests have the correct size whether the structural parameters are identified or not. Relating to the current tests, the application of ours is appropriate especially to models whose moment conditions are nonlinear in parameters. Moreover, the computation of ours tests is simple, allowing their implementation in a large number of statistical software packages. We compare our tests with Wald tests by performing simulation experiments. We use our tests to analyze the female labor supply and the demand for cigarette.

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File URL: http://econ.tulane.edu/RePEc/pdf/tul0801.pdf
File Function: First version, 2008
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File URL: http://econ.tulane.edu/RePEc/pdf/tul0801r1.pdf
File Function: Revised version, 2009
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Bibliographic Info

Paper provided by Tulane University, Department of Economics in its series Working Papers with number 0801.

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Length: 31 pages
Date of creation: Sep 2008
Date of revision: Apr 2009
Handle: RePEc:tul:wpaper:0801

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Related research

Keywords: weak identification; minimum chi-square estimation; hypothesis testing; limited dependent variable models;

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References

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  1. Blundell, Richard & MaCurdy, Thomas & Meghir, Costas, 2007. "Labor Supply Models: Unobserved Heterogeneity, Nonparticipation and Dynamics," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 69 Elsevier.
  2. Blundell, Richard W & Smith, Richard J, 1989. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models," Review of Economic Studies, Wiley Blackwell, vol. 56(1), pages 37-57, January.
  3. Sbordone, Argia M., 2005. "Do expected future marginal costs drive inflation dynamics?," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1183-1197, September.
  4. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
  5. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-60, November.
  6. Frank Kleibergen, 2002. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Econometrica, Econometric Society, vol. 70(5), pages 1781-1803, September.
  7. Andrew M. Jones & José M. Labeaga, 2003. "Individual heterogeneity and censoring in panel data estimates of tobacco expenditure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 157-177.
  8. Santos Silva, J M C, 2001. "Influence Diagnostics and Estimation Algorithms for Powell's SCLS," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 55-62, January.
  9. Guggenberger, Patrik & Smith, Richard J., 2005. "Generalized Empirical Likelihood Estimators And Tests Under Partial, Weak, And Strong Identification," Econometric Theory, Cambridge University Press, vol. 21(04), pages 667-709, August.
  10. Lee, Myoung-Jae, 1992. "Winsorized Mean Estimator for Censored Regression," Econometric Theory, Cambridge University Press, vol. 8(03), pages 368-382, September.
  11. 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.
  12. Newey, Whitney K., 1987. "Efficient estimation of limited dependent variable models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 36(3), pages 231-250, November.
  13. Kleibergen, Frank, 2007. "Generalizing weak instrument robust IV statistics towards multiple parameters, unrestricted covariance matrices and identification statistics," Journal of Econometrics, Elsevier, vol. 139(1), pages 181-216, July.
  14. Thomas Mroz, . "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," University of Chicago - Population Research Center 84-8, Chicago - Population Research Center.
  15. Donald W.K. Andrews & Gustavo Soares, 2006. "Rank Tests for Instrumental Variables Regression with Weak Instruments," Cowles Foundation Discussion Papers 1564, Cowles Foundation for Research in Economics, Yale University.
  16. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, 07.
  17. John Mullahy, 1997. "Instrumental-Variable Estimation Of Count Data Models: Applications To Models Of Cigarette Smoking Behavior," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 586-593, November.
  18. Arellano, M. & Bover, O. & Labeaga, J.M., 1997. "Autoregressive Models with Sample Selectivity for Panel Data," Papers 9706, Centro de Estudios Monetarios Y Financieros-.
  19. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, 07.
  20. Smith, Richard J & Blundell, Richard W, 1986. "An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply," Econometrica, Econometric Society, vol. 54(3), pages 679-85, May.
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Cited by:
  1. Jean-Marie Dufour & Joachim Wilde, 2013. "Weak Identification in Probit Models with Endogenous Covariates," Working Papers 95, Institute of Empirical Economic Research, revised 28 Feb 2013.
  2. Stephen C. Smith & M. Shahe Emran & Fenohasina Maret, 2009. "Education and Freedom of Choice: Evidence from Arranged Marriages in Vietnam," Working Papers 2009-15, The George Washington University, Institute for International Economic Policy.

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