Book Chapter Details
Mandatory Fields
Conniffe, D;O'Neill, D
2011 January
MISSING DATA METHODS: CROSS-SECTIONAL METHODS AND APPLICATIONS
EFFICIENT PROBIT ESTIMATION WITH PARTIALLY MISSING COVARIATES
EMERALD GROUP PUBLISHING LTD
BINGLEY
Published
1
Optional Fields
SURROGATE OUTCOME DATA EXPLANATORY VARIABLES PORTFOLIO CHOICE SAMPLE SELECTION INCOMPLETE-DATA RISK-AVERSION REGRESSION MODELS AUXILIARY INFERENCE
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.
0731-9053
209
245
10.1108/S0731-9053(2011)000027A011
Grant Details