Efficient Probit Estimation with Partially Missing Covariates

Denis Conniffe and Donal O'Neill

No 4081, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: A 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.

Keywords: risk aversion; probit model; portfolio allocation; missing data (search for similar items in EconPapers)
JEL-codes: C25 G11 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2009-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)

Published - published in: D. Drukker (ed.): Advances in Econometrics, Vol. 27A, Missing Data Methods, 2011, 213-249.

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Chapter: Efficient Probit Estimation with Partially Missing Covariates (2011)
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