Conniffe, Denis and O'Neill, Donal
Efficient Probit Estimation with Partially Missing Covariates.
IZA Discussion Paper No. 4081.
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
||missing data; probit model; portfolio allocation; risk aversion;
||Faculty of Social Sciences > Economics, Finance and Accounting
Prof. Donal O'Neill
||17 Apr 2012 15:52
|Journal or Publication Title:
||IZA Discussion Paper No. 4081
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