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http://hdl.handle.net/10419/35727
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| Title: | | Efficient probit estimation with partially missing covariates  |
| Authors: | | Conniffe, Denis O'Neill, Donal |
| Issue Date: | | 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: | | urn:nbn:de:101:1-20090330147 |
| Document Type: | | Working Paper |
| Appears in Collections: | | IZA Discussion Papers, Forschungsinstitut zur Zukunft der Arbeit (IZA)
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