Efficient Probit Estimation with Partially Missing Covariates

Denis Conniffe and Donal O'Neill

A chapter in Missing Data Methods: Cross-sectional Methods and Applications, 2011, pp 209-245 from Emerald Group Publishing Limited

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 parametric assumptions adopted in the benchmark case. We illustrate our estimator by examining the portfolio allocation decision of Italian households.

Keywords: Missing data; probit model; portfolio allocation; risk-aversion (search for similar items in EconPapers)
Date: 2011
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Working Paper: Efficient Probit Estimation with Partially Missing Covariates (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2011)000027a011

DOI: 10.1108/S0731-9053(2011)000027A011

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