※ [本文转录自 NTUfinGrad00 看板 #1CvaCeCy ] 作者: cretantu (计量理论与应用研究中心) 看板: NTUfinGrad00 标题: [情报] CRETA十一月份WETA第二场研讨会讯息 时间: Fri Nov 19 17:28:06 2010 台大计量理论与应用研究中心 (CRETA)、台湾经济计量学会与台大财务金融学系， 将共同举办十一月份Workshop on Econometrics:Theory and Application(WETA@TES)。 【2010 年十一月份第二场 WETA 研讨会】 日期：2010 年 11 月 26 日 地点：台湾大学管理学院一号馆 2F 冠德讲堂 主讲人：陈宜廷博士 (中央研究院经济研究所) 时间：14:00~15:15 session 1 15:15~15:45 茶 叙 15:45~17:00 session 2 讲题：Maximum Entropy Principle: Review and Applications 讲题摘要： 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. 讲者介绍： 陈宜廷教授为台湾大学经济学博士，目前任职於中央研究院经济研究所， 研究领域为 Econometrics, Time Series Analysis, Empirical Finance。 WETA 不需事先报名，欢迎各位踊跃参加!! 也欢迎大家介绍非会员朋友加入台湾经济计量学会与 WETA。 如有问题，欢迎来信或来电 ( E-mail: <[email protected] >; Tel: 02-3366-1072) --

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