An Efficient Estimator for Dealing with Missing Data on Explanatory Variables in a Probit Choice Model
AbstractA 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
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Bibliographic InfoPaper provided by Department of Economics, Finance and Accounting, National University of Ireland - Maynooth in its series Economics, Finance and Accounting Department Working Paper Series with number n1960908.pdf.
Length: 36 pages
Date of creation: 2008
Date of revision:
Missing Data; Probit Model; Portfolio Allocation; Risk Aversion;
Find related papers by JEL classification:
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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