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    Efficient Probit Estimation with Partially Missing Covariates


    Conniffe, Denis and O'Neill, Donal (2009) Efficient Probit Estimation with Partially Missing Covariates. IZA Discussion Paper No. 4081.

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    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

    Item Type: Article
    Keywords: missing data; probit model; portfolio allocation; risk aversion;
    Academic Unit: Faculty of Social Sciences > Economics, Finance and Accounting
    Item ID: 3580
    Depositing User: Donal O'Neill
    Date Deposited: 17 Apr 2012 15:52
    Journal or Publication Title: IZA Discussion Paper No. 4081
    Refereed: Yes
    URI:

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