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Weighted Semiparametric Estimator for Binary Response Models

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dc.contributor.advisor Ker, Alan
dc.contributor.advisor McNicholas, Paul Ramadan, Anas 2013-06-17T18:34:07Z 2013-06-17T18:34:07Z 2013-04 2013-05-15 2013-06-17
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.title Weighted Semiparametric Estimator for Binary Response Models en_US
dc.type Thesis en_US Mathematics and Statistics en_US Master of Science en_US Department of Mathematics and Statistics en_US
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