Conniffe, Denis and O'Neill, Donal
An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model.
Department of Economics Finance & Accounting.
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.
||Part of the Department of Economics Finance & Accounting working paper series N1960908
||Missing Data, Probit Model, Portfolio Allocation, Risk Aversion
||Social Sciences > Economics
Ms Sandra Doherty
||24 Sep 2008
||Department of Economics Finance & Accounting
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