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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

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    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," Review of Economic Studies, Oxford University Press, 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. Conniffe, Denis & O'Neill, Donal, 2009. "Efficient Probit Estimation with Partially Missing Covariates," IZA Discussion Papers 4081, Institute of Labor Economics (IZA).
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    binary variable; imputation; OLS; heteroskedasticity;

    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

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