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Efficient Probit Estimation with Partially Missing Covariates

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  • Conniffe, Denis

    (National University of Ireland, Maynooth)

  • O'Neill, Donal

    (National University of Ireland, Maynooth)

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 benchmark case. We illustrate our estimator by examining the portfolio allocation decision of Italian households.

Suggested Citation

  • Conniffe, Denis & O'Neill, Donal, 2009. "Efficient Probit Estimation with Partially Missing Covariates," IZA Discussion Papers 4081, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp4081
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    4. 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.
    5. 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.

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    More about this item

    Keywords

    risk aversion; probit model; portfolio allocation; missing data;
    All these keywords.

    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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