IDEAS home Printed from https://ideas.repec.org/p/tul/wpaper/0801.html
   My bibliography  Save this paper

Inference in Limited Dependent Variable Models Robust to Weak Identification

Author

Listed:
  • 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.

Suggested Citation

  • Leandro M. Magnusson, 2008. "Inference in Limited Dependent Variable Models Robust to Weak Identification," Working Papers 0801, Tulane University, Department of Economics, revised Apr 2009.
    • Handle: RePEc:tul:wpaper:0801
    as

    Download full text from publisher

    File URL: http://repec.tulane.edu/RePEc/pdf/tul0801.pdf
    File Function: First version, 2008
    Download Restriction: no
    File URL: http://repec.tulane.edu/RePEc/pdf/tul0801r1.pdf
    File Function: Revised version, 2009
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-1460, November.
    2. 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(4), pages 667-709, August.
    3. Li, Hong, 2008. "Estimation and testing of Euler equation models with time-varying reduced-form coefficients," Journal of Econometrics, Elsevier, vol. 142(1), pages 425-448, January.
    4. 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.
    5. 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.
    6. 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 679-685, May.
    7. Andrews, Donald W.K. & Soares, Gustavo, 2007. "Rank Tests For Instrumental Variables Regression With Weak Instruments," Econometric Theory, Cambridge University Press, vol. 23(6), pages 1033-1082, December.
    8. Marcelo J. Moreira, 2003. "A Conditional Likelihood Ratio Test for Structural Models," Econometrica, Econometric Society, vol. 71(4), pages 1027-1048, July.
    9. Olympia Bover & Manuel Arellano, 1997. "Estimating limited dependent variable models from panel data," Investigaciones Economicas, Fundación SEPI, vol. 21(2), pages 141-166, May.
    10. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    11. Lee, Myoung-Jae, 1995. "Semi-parametric Estimation of Simultaneous Equations with Limited Dependent Variables: A Case Study of Female Labour Supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 187-200, April-Jun.
    12. Lee, Myoung-Jae, 1992. "Winsorized Mean Estimator for Censored Regression," Econometric Theory, Cambridge University Press, vol. 8(3), pages 368-382, September.
    13. Manuel Arellano & Olympia Bover & José M. Labeaga, 1997. "Authoregressive Models with Sample Selectivity for Panel Data," Working Papers wp1997_9706, CEMFI.
    14. Sbordone, Argia M., 2005. "Do expected future marginal costs drive inflation dynamics?," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1183-1197, September.
    15. 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.
    16. 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.
    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. Gourieroux, C. & Monfort, A., 1989. "A General Framework for Testing a Null Hypothesis in a “Mixed” Form," Econometric Theory, Cambridge University Press, vol. 5(1), pages 63-82, April.
    19. Mroz, Thomas A, 1987. "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions," Econometrica, Econometric Society, vol. 55(4), pages 765-799, July.
    20. Richard W. Blundell & Richard J. Smith, 1989. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 56(1), pages 37-57.
    21. 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.
    22. Frank Kleibergen, 2005. "Testing Parameters in GMM Without Assuming that They Are Identified," Econometrica, Econometric Society, vol. 73(4), pages 1103-1123, July.
    23. Frank Kleibergen, 2002. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Econometrica, Econometric Society, vol. 70(5), pages 1781-1803, September.
    24. 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.
    25. Amemiya, Takeshi, 1979. "The Estimation of a Simultaneous-Equation Tobit Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 20(1), pages 169-181, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lanz, Bruno & Provins, Allan, 2017. "Using averting expenditures to estimate the demand for public goods: Combining objective and perceived quality," Resource and Energy Economics, Elsevier, vol. 47(C), pages 20-35.
    2. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    3. Antonio Diez de Los Rios, 2015. "A New Linear Estimator for Gaussian Dynamic Term Structure Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 282-295, April.
    4. Jean-Marie Dufour & Joachim Wilde, 2018. "Weak identification in probit models with endogenous covariates," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(4), pages 611-631, October.
    5. Barber, Luke & Jetter, Michael & Krieger, Tim, 2023. "Foreshadowing Mars: Religiosity and Pre-enlightenment Warfare," IZA Discussion Papers 16586, Institute of Labor Economics (IZA).
    6. David T. Frazier & Eric Renault & Lina Zhang & Xueyan Zhao, 2020. "Weak Identification in Discrete Choice Models," Papers 2011.06753, arXiv.org, revised Jan 2021.
    7. Chuhui Li & Donald S. Poskitt & Frank Windmeijer & Xueyan Zhao, 2022. "Binary outcomes, OLS, 2SLS and IV probit," Econometric Reviews, Taylor & Francis Journals, vol. 41(8), pages 859-876, September.
    8. Jetter, Michael & Walker, Jay K., 2022. "News coverage and mass shootings in the US," European Economic Review, Elsevier, vol. 148(C).
    9. Wendy Correa Martínez & Michael Jetter, 2016. "Isolating causality between gender and corruption: An IV approach," Documentos de Trabajo de Valor Público 14438, Universidad EAFIT.
    10. Jetter, Michael, 2017. "The effect of media attention on terrorism," Journal of Public Economics, Elsevier, vol. 153(C), pages 32-48.
    11. M. Shahe Emran & Fenohasina Maret-Rakotondrazaka & Stephen C. Smith, 2014. "Education and Freedom of Choice: Evidence from Arranged Marriages in Vietnam," Journal of Development Studies, Taylor & Francis Journals, vol. 50(4), pages 481-501, April.
    12. Dakyung Seong, 2022. "Binary response model with many weak instruments," Papers 2201.04811, arXiv.org, revised May 2023.
    13. Fiorini, Luciana C. & Jetter, Michael & Parmeter, Christopher F. & Parsons, Christopher, 2020. "The Effect of Community Size on Electoral Preferences: Evidence From Post-WWII Southern Germany," IZA Discussion Papers 13724, Institute of Labor Economics (IZA).
    14. Gregory Cox, 2020. "Weak Identification with Bounds in a Class of Minimum Distance Models," Papers 2012.11222, arXiv.org, revised Dec 2022.
    15. repec:gii:ciesrp:cies_rp_36rev is not listed on IDEAS
    16. Aparicio, Juan P. & Jetter, Michael, 2020. "Captivating News in Colombia," IZA Discussion Papers 13834, Institute of Labor Economics (IZA).
    17. Aparicio, Juan P. & Jetter, Michael, 2022. "Captivating news: Media attention and FARC kidnappings," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 69-81.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leandro M. Magnusson, 2008. "Tests in Censored Models when the Structural Parameters Are Not Identified," Working Papers 0802, Tulane University, Department of Economics.
    2. Andrews, Donald W.K. & Guggenberger, Patrik, 2010. "Applications of subsampling, hybrid, and size-correction methods," Journal of Econometrics, Elsevier, vol. 158(2), pages 285-305, October.
    3. Murray Michael P., 2017. "Linear Model IV Estimation When Instruments Are Many or Weak," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-22, January.
    4. Guggenberger, Patrik & Smith, Richard J., 2008. "Generalized empirical likelihood tests in time series models with potential identification failure," Journal of Econometrics, Elsevier, vol. 142(1), pages 134-161, January.
    5. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    6. Isaiah Andrews & Timothy B. Armstrong, 2017. "Unbiased instrumental variables estimation under known first‐stage sign," Quantitative Economics, Econometric Society, vol. 8(2), pages 479-503, July.
    7. Donald W.K. Andrews & James H. Stock, 2005. "Inference with Weak Instruments," NBER Technical Working Papers 0313, National Bureau of Economic Research, Inc.
    8. Joel L. Horowitz, 2018. "Non-Asymptotic Inference in Instrumental Variables Estimation," Papers 1809.03600, arXiv.org.
    9. Kleibergen, Frank, 2009. "Tests of risk premia in linear factor models," Journal of Econometrics, Elsevier, vol. 149(2), pages 149-173, April.
    10. Andrews, Donald W.K. & Cheng, Xu, 2014. "Gmm Estimation And Uniform Subvector Inference With Possible Identification Failure," Econometric Theory, Cambridge University Press, vol. 30(2), pages 287-333, April.
    11. Doko Tchatoka, Firmin, 2011. "Testing for partial exogeneity with weak identification," MPRA Paper 39504, University Library of Munich, Germany, revised Mar 2012.
    12. Leandro M. Magnusson & Sophocles Mavroeidis, 2010. "Identification-Robust Minimum Distance Estimation of the New Keynesian Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(2-3), pages 465-481, March.
    13. Moreira, Humberto & Moreira, Marcelo J., 2019. "Optimal two-sided tests for instrumental variables regression with heteroskedastic and autocorrelated errors," Journal of Econometrics, Elsevier, vol. 213(2), pages 398-433.
    14. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.
    15. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP46/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Guggenberger, Patrik & Ramalho, Joaquim J.S. & Smith, Richard J., 2012. "GEL statistics under weak identification," Journal of Econometrics, Elsevier, vol. 170(2), pages 331-349.
    17. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers 46/17, Institute for Fiscal Studies.
    18. Cheng, Xu, 2015. "Robust inference in nonlinear models with mixed identification strength," Journal of Econometrics, Elsevier, vol. 189(1), pages 207-228.
    19. Joel L. Horowitz, 2018. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP52/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.

    More about this item

    Keywords

    weak identification; minimum chi-square estimation; hypothesis testing; limited dependent variable models;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tul:wpaper:0801. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kerui Geng (email available below). General contact details of provider: https://edirc.repec.org/data/detulus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.