Efficient Probit Estimation with Partially Missing Covariates
Missing Data Methods: Cross-sectional Methods and Applications
ISBN: 978-1-78052-524-2, eISBN: 978-1-78052-525-9
Publication date: 23 November 2011
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
Citation
Conniffe, D. and O'Neill, D. (2011), "Efficient Probit Estimation with Partially Missing Covariates", Drukker, D.M. (Ed.) Missing Data Methods: Cross-sectional Methods and Applications (Advances in Econometrics, Vol. 27 Part 1), Emerald Group Publishing Limited, Leeds, pp. 209-245. https://doi.org/10.1108/S0731-9053(2011)000027A011
Publisher
:Emerald Group Publishing Limited
Copyright © 2011, Emerald Group Publishing Limited