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

Denis Conniffe and Donal O’Neill ()
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Donal O’Neill: Economics, National University of Ireland, Maynooth
Authors registered in the RePEc Author Service: Donal O'Neill

Economics Department Working Paper Series from Department of Economics, National University of Ireland - Maynooth

Abstract: 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

Keywords: Missing Data; Probit Model; Portfolio Allocation; Risk Aversion (search for similar items in EconPapers)
JEL-codes: C25 G11 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2008
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

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