IDEAS home Printed from https://ideas.repec.org/p/hhs/oruesi/2015_003.html
   My bibliography  Save this paper

A Two-Step Estimator for Missing Values in Probit Model Covariates

Author

Listed:
  • Laitila, Thomas

    (Örebro University School of Business)

  • Wang, Lisha

    (Örebro University School of Business)

Abstract

This paper includes a simulation study on the bias and MSE properties of a two-step probit model estimator for handling missing values in covariates by conditional imputation. In one smaller simulation it is compared with an asymptotically ecient estimator and in one larger it is compared with the probit ML on complete cases after listwise deletion. Simulation results obtained favors the use of the two-step probit estimator and motivates further developments of the methodology.

Suggested Citation

  • Laitila, Thomas & Wang, Lisha, 2015. "A Two-Step Estimator for Missing Values in Probit Model Covariates," Working Papers 2015:3, Örebro University, School of Business.
    • Handle: RePEc:hhs:oruesi:2015_003
    as

    Download full text from publisher

    File URL: https://www.oru.se/globalassets/oru-sv/institutioner/hh/workingpapers/workingpapers2015/wp-3-2015.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    2. Dagenais, Marcel G., 1973. "The use of incomplete observations in multiple regression analysis : A generalized least squares approach," Journal of Econometrics, Elsevier, vol. 1(4), pages 317-328, December.
    3. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    4. Heij, Christiaan & de Boer, Paul & Franses, Philip Hans & Kloek, Teun & van Dijk, Herman K., 2004. "Econometric Methods with Applications in Business and Economics," OUP Catalogue, Oxford University Press, number 9780199268016.
    5. Christian Gourieroux & Alain Monfort, 1981. "On the Problem of Missing Data in Linear Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 48(4), pages 579-586.
    6. Laitila, Thomas, 1993. "A pseudo-R2 measure for limited and qualitative dependent variable models," Journal of Econometrics, Elsevier, vol. 56(3), pages 341-355, April.
    7. Denis Conniffe & Donal O'Neill, 2011. "Efficient Probit Estimation with Partially Missing Covariates," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 209-245, Emerald Group Publishing Limited.
    Full references (including those not matched with items on IDEAS)

    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. Denis Conniffe & Donal O'Neill, 2011. "Efficient Probit Estimation with Partially Missing Covariates," Advances in Econometrics, in: Missing Data Methods: Cross-sectional Methods and Applications, pages 209-245, Emerald Group Publishing Limited.
    2. Palm, Franz C & Nijman, Theo E, 1984. "Missing Observations in the Dynamic Regression Model," Econometrica, Econometric Society, vol. 52(6), pages 1415-1435, November.
    3. Maria Raikou & Alistair McGuire, 2012. "Estimating Costs for Economic Evaluation," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 43, Edward Elgar Publishing.
    4. Lee, Jonq-Ying, 1986. "Imputed Missing Incomes And Marginal Propensity To Consume Food," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 11(2), pages 1-8, December.
    5. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    6. Helen Jensen & Justo Manrique, 1998. "Demand for food commodities by income groups in Indonesia," Applied Economics, Taylor & Francis Journals, vol. 30(4), pages 491-501.
    7. Chris Muris, 2020. "Efficient GMM Estimation with Incomplete Data," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 518-530, July.
    8. Heien, Dale & Pompelli, Greg, 1988. "The Demand For Beef Products: Cross-Section Estimation Of Demographic And Economic Effects," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 13(1), pages 1-8, July.
    9. Denis Conniffe & Donal O’Neill, 2008. "An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model," Economics Department Working Paper Series n1960908.pdf, Department of Economics, National University of Ireland - Maynooth.
    10. Qian, Hang, 2010. "Linear regression using both temporally aggregated and temporally disaggregated data: Revisited," MPRA Paper 32686, University Library of Munich, Germany.
    11. Vredin Johansson, Maria & Heldt, Tobias & Johansson, Per, 2006. "The effects of attitudes and personality traits on mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 507-525, July.
    12. Adele Bergin, 2015. "Employer Changes and Wage Changes: Estimation with Measurement Error in a Binary Variable," LABOUR, CEIS, vol. 29(2), pages 194-223, June.
    13. Imbens, Guido W. & Pizer, William A., 2000. "The Analysis of Randomized Experiments with Missing Data," Discussion Papers 10596, Resources for the Future.
    14. Fernando Rios-Avila & Gustavo Canavire-Bacarreza, 2018. "Standard-error correction in two-stage optimization models: A quasi–maximum likelihood estimation approach," Stata Journal, StataCorp LP, vol. 18(1), pages 206-222, March.
    15. Ana Nuevo-Chiquero, 2014. "Out-of-Wedlock Fertility, Post-Pregnancy Choices and Contraceptive Usage," Working Papers 2014009, The University of Sheffield, Department of Economics.
    16. Sofie Balcaen & Sophie Manigart & Hubert Ooghe, 2011. "From distress to exit: determinants of the time to exit," Journal of Evolutionary Economics, Springer, vol. 21(3), pages 407-446, August.
    17. Allais, Olivier & Etilé, Fabrice & Lecocq, Sébastien, 2015. "Mandatory labels, taxes and market forces: An empirical evaluation of fat policies," Journal of Health Economics, Elsevier, vol. 43(C), pages 27-44.
    18. Hyojoung Kim & Doyoung Kim & Subin Im & James W. Hardin, 2009. "Evidence of Asymmetric Information in the Automobile Insurance Market: Dichotomous Versus Multinomial Measurement of Insurance Coverage," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(2), pages 343-366, June.
    19. Klos, Alexander & Rottke, Simon, 2013. "Saving and Consumption When Children Move Out," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79786, Verein für Socialpolitik / German Economic Association.
    20. Richard Harris & John Moffat, 2011. "R&D, Innovation and Exporting," SERC Discussion Papers 0073, Centre for Economic Performance, LSE.

    More about this item

    Keywords

    binary variable; imputation; OLS; heteroskedasticity;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:hhs:oruesi:2015_003. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/ieoruse.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.