Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/35727
Authors: 
Conniffe, Denis
O'Neill, Donal
Year of Publication: 
2009
Series/Report no.: 
IZA discussion papers 4081
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.
Subjects: 
Missing data
probit model
portfolio allocation
risk aversion
JEL: 
C25
G11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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