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    An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model

    Conniffe, Denis and O'Neill, Donal (2008) An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model. Working Paper. Department of Economics Finance & Accounting. (Unpublished)

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    A common approach to dealing with missing data in econometrics is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. In this paper we consider a particular pattern of missing data on explanatory variables that often occurs in practice and develop a new efficient estimator for models where the dependent variable is binary. We derive exact formulae for the estimator and its asymptotic variance. Simulation results show that our estimator performs well when compared to popular alternatives, such as complete case analysis and multiple imputation. We then use our estimator to examine the portfolio allocation decision of Italian households using the Survey of Household Income and Wealth carried out by the Bank of Italy.

    Item Type: Monograph (Working Paper)
    Additional Information: Part of the Department of Economics Finance & Accounting working paper series N1960908
    Keywords: Missing Data, Probit Model, Portfolio Allocation, Risk Aversion
    Academic Unit: Faculty of Social Sciences > Economics, Finance and Accounting
    Item ID: 1049
    Depositing User: Ms Sandra Doherty
    Date Deposited: 24 Sep 2008
    Publisher: Department of Economics Finance & Accounting
    Refereed: No
      Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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