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New PDF release: Festschrift in Honor of Peter Schmidt: Econometric Methods

By Robin C. Sickles, William C. Horrace

ISBN-10: 1489980075

ISBN-13: 9781489980076

ISBN-10: 1489980083

ISBN-13: 9781489980083

From Robin Sickles: As I indicated to you a few months in the past Professor William Horrace and that i would prefer Springer to post a Festschrift in Honor of Peter Schmidt, our professor. Peter’s accomplishments are mythical between his scholars and the career. i've got a little that scholar viewpoint in my introductory and shutting comments at the site for the convention we had in his honor this final July. i've got hooked up the convention application from which chosen papers will come (as good as from scholars who have been not able to attend). additionally, you will locate the names of his scholars (40) at the web site. A best twenty economics division might be begun up from these forty scholars. Papers from a few festschrifts have a thematic hyperlink one of the papers in keeping with subject matter. What i believe is exclusive to this festschrift is that the subject matter operating throughout the papers should be Peter’s notable legacy left to his scholars to border an issue after which learn and think about it extensive utilizing rigorous ideas yet infrequently only for the aim of showcasing technical refinements in keeping with se. i feel this might be a e-book that graduate scholars might locate valuable of their early learn careers and professional students may locate worthy in either their and their scholars’ research.

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Additional info for Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications

Sample text

Here the results are generally far less reassuring. The performance of endogeneity tests in the linear instrumental variables models is poor,9 with particularly misleading results in the case of the Hausman test. The performance of the bivariate probit model, Residual1 and Residual2 are not much better. In general, these results suggest that conventional endogeneity tests are more or less completely unreliable, at least in terms of conventional benchmark p-value thresholds when both dependent variables are binary.

As expected, BIPROBIT performs well under these circumstances when the true error distribution is bivariate normal. However, Residual2 performs as well or is even slightly better than BIPROBIT. In addition, even when the true error distribution is bivariate normal, DFM represents a significant improvement over LPM and performs only slightly worse than Residual2 and BIPROBIT. When the true error distribution is non-normal, DFM dominates all other estimators. The only estimation methods that come close are the linear instrumental variables estimators, which are also robust to non-normal errors.

This table clearly isolates the cases in which the linear instrumental variables estimators perform relatively well. We see that all three linear instrumental variables estimators are inferior to LPM when the error correlation is low regardless of instrument strength. K. M. 1. 3 and instrument strength is high. We do not display results for instrument strength 25 but in this case, the linear instrumental variables estimators offer slight improvement over LPM with the higher error correlation. This relatively strong performance for the linear instrumental variables methods is robust to a further stratification by error distribution (results not displayed).

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Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications by Robin C. Sickles, William C. Horrace


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