[情報] CRETA十一月份WETA第二場研討會뀠… - 經濟

※ [本文轉錄自 Econ-PHD 看板 #1CvaHdJj ]

作者: janechen (CCC) 看板: Econ-PHD
標題: [轉錄][情報] CRETA十一月份WETA第二場研討會訊息
時間: Fri Nov 19 17:33:25 2010

※ [本文轉錄自 NTUfinGrad00 看板 #1CvaCeCy ]

作者: cretantu (計量理論與應用研究中心) 看板: NTUfinGrad00
標題: [情報] CRETA十一月份WETA第二場研討會訊息
時間: Fri Nov 19 17:28:06 2010

臺大計量理論與應用研究中心 (CRETA)、臺灣經濟計量學會與臺大財務金融學系，
將共同舉辦十一月份Workshop on Econometrics:Theory and Application(WETA@TES)。

【2010 年十一月份第二場 WETA 研討會】
日期：2010 年 11 月 26 日
地點：臺灣大學管理學院一號館 2F 冠德講堂
主講人：陳宜廷博士 (中央研究院經濟研究所)
時間：14:00~15:15 session 1
15:15~15:45 茶 敘
15:45~17:00 session 2
講題：Maximum Entropy Principle: Review and Applications

講題摘要：
The maximum likelihood (ML) method is known as the best statistical inference
method in the case where the true data generating process (DGP) is known.
Many parametric specification, estimation, and testing methods explicitly or
implicitly claim their optimality following the ML principle. However, the fact
is that the true DGP is unknown. A more realistic situation is that we could
only learn partial information about the real world either from economic
theories or statistical observations. Put differently, although the ML
principle is a golden rule in theory, it is infeasible in practice. This fact
has considerably motivated the use and development of the method of moments
(MM) and its extensions and variants, like the generalized MM (GMM) and the
quasi-ML(QML) methods, in econometrics. A common feature of these robust
methods is that they do not rely on, and hence do not pursue, a complete
(conditional) distribution specification for parameter estimation.
However, we do need a complete distribution specification in many economic and
financial problems. In this scenario, the maximum entropy (MaxEnt) principle
is useful because it allows us to recover a distribution specification from a
set of data-consistent, or theory-consistent, moment conditions in a
"least-biased" way.

In the first part of this lecture, we will review some key concepts and
appealing properties of the MaxEnt principle, discuss the associated
implementation issues, and provide personal discussions about this approach.
In the second part, we will discuss some existing econometric applications
and introduce personal studies of this principle.

講者介紹：
陳宜廷教授為臺灣大學經濟學博士，目前任職於中央研究院經濟研究所，
研究領域為 Econometrics, Time Series Analysis, Empirical Finance。

WETA 不需事先報名，歡迎各位踴躍參加!!
也歡迎大家介紹非會員朋友加入臺灣經濟計量學會與 WETA。
如有問題，歡迎來信或來電 ( E-mail: <[email protected] >; Tel: 02-3366-1072)

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