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Efficient Probit Estimation with Partially Missing Covariates

In: Missing Data Methods: Cross-sectional Methods and Applications

Author

Listed:
  • Denis Conniffe
  • Donal O'Neill

Abstract

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of conditional multinormality we show that our estimator is efficient and provide exact formulae for its asymptotic variance. Simulation results show that our estimator outperforms popular alternatives and is robust to departures from the parametric assumptions adopted in the benchmark case. We illustrate our estimator by examining the portfolio allocation decision of Italian households.

Suggested Citation

  • 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.
    • Handle: RePEc:eme:aecozz:s0731-9053(2011)000027a011
    DOI: 10.1108/S0731-9053(2011)000027A011
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    Cited by:

    1. is not listed on IDEAS
    2. Martin Iseringhausen & Ms. Mwanza Nkusu & Wellian Wiranto, 2019. "Repeated Use of IMF-Supported Programs: Determinants and Forecasting," IMF Working Papers 2019/245, International Monetary Fund.
    3. Michael Wosser, 2015. "Long Run Macroeconomic and Sectoral Determinants of Systemic Banking Crises," Economics Department Working Paper Series n266-15.pdf, Department of Economics, National University of Ireland - Maynooth.
    4. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    5. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    6. 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.

    More about this item

    Keywords

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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