推 Wind1026:管中閔老師的老師! 03/07 23:33
大家應該都聽過「White noise」吧 XD
白先生本尊要來台大演講啦 XDDD
Date: Mar. 26, 2011 (Sat.), 14:00 pm – 15:50 pm
Venue: 台灣大學管理學院一號館 2F 重光講堂
Topics: Robustness Checks and Robustness Tests in Applied Economics
Speaker: Professor Halbert White, University of California, San Diego
Registration:
台灣大學在學學生及現任教職員和台灣經濟計量學會會員為免費參加
其他參加者報名費為 NT$600
欲參加者，請於 3 月 24日 (四)中午前至 CRETA 網站線上報名
<http://www.creta.org.tw/events/view/32>
(當天將開放現場繳交台灣經濟計量學會 2011 年年度會費)
[About the Speaker]
Professor White is currently Chancellor’s Associates Distinguished Professor
of Economics at University of California, San Diego. He is one of the world’
s leading economists and is noted for pioneering research standards. His
particular expertise is in Econometric Theory, Forecasting, Artificial Neural
Networks, and Financial Markets. Professor White has published several
well-known books,such as “Asymptotic Theory for Econometricians”, “
Estimation, Inference, and Specification Analysis”, “New Perspectives in
Econometric Theory” and more than 100 articles in the top-notch journals:
Econometrica, Journal of Political Economy, Journal of Econometrics, Journal
of Finance and Journal of the American Statistical Association. One of his
greatest accomplishments, the White’s Test, has become widely used by
economists and thus made this paper one of the most cited articles
in economics. Professor White was a Guggenheim fellow and also an elected
fellow of the American Academy of Arts and Sciences and the Econometric
Society.
[Lecture Overview]
The paper studies when and how one can infer structural validity from
coefficient robustness and plausibility and discusses how critical and
non-critical core variables can be properly specified and how non-core
variables forthe comparison regression can be chosen to ensure that
robustness checks are indeed structurally informative. The paper proposes a
new Hausman (1978)-type test of robustness, additional diagnostics that can
help explain why robustness test rejection occurs, and a new estimator, the
Feasible Optimally combined GLS (FOGLeSs) estimator.
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