Efficient Probit Estimation with Partially Missing Covariates
AbstractA 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.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4081.
Length: 42 pages
Date of creation: Mar 2009
Date of revision:
Publication status: published in: D. Drukker (ed.): Advances in Econometrics, Vol. 27A, Missing Data Methods, 2011, 213-249.
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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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