Conniffe, Denis and O'Neill, Donal (2009) Efficient Probit Estimation with Partially Missing Covariates. IZA Discussion Paper No. 4081.

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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

Item Type:	Article
Keywords:	missing data; probit model; portfolio allocation; risk aversion;
Academic Unit:	Faculty of Social Sciences > Economics, Finance and Accounting
Item ID:	3580
Depositing User:	Donal O'Neill
Date Deposited:	17 Apr 2012 15:52
Journal or Publication Title:	IZA Discussion Paper No. 4081
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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