TY  - RPRT
        AU  - Conniffe, Denis
AU  - O'Neill, Donal
        TI  - Efficient Probit Estimation with Partially Missing Covariates
        PY  - 2009/Mar/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 4081
        UR  - https://www.iza.org/publications/dp4081
        AB  - 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.
        KW  - risk aversion
KW  - probit model
KW  - portfolio allocation
KW  - missing data
        ER  -