Weighted Semiparametric Estimator for Binary Response Models
This thesis proposes a new estimator, namely, a weighted semiparametric estimator (WSPE) for binary response models incorporating both the probit and single index model (SIM). Appropriate weights for the probit and SIM are estimated via bayesian model averaging (BMA). The assigned weights are propor...
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| Language: | en |
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2013
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| Online Access: | http://hdl.handle.net/10214/7248 |
| Summary: | This thesis proposes a new estimator, namely, a weighted semiparametric estimator (WSPE) for binary response models incorporating both the probit and single index model (SIM). Appropriate weights for the probit and SIM are estimated via bayesian model averaging (BMA). The assigned weights are proportional to the information obtained from maximum likelihood (ML) values of the SIM and the probit, respectively; these ML values are then used to calculate the marginal likelihood in the BMA. The finite sample performance of the WSPE is compared to the performance of the probit and SIM. Simulation results of this research show that the WSPE achieves significant bias reduction and up to 46% gain in efficiency. The results of the empirical application show that the WSPE performs as well as the probit when the data meets the probit assumptions, and as well as the SIM otherwise. |
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