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計量経済学

計量経済学セミナーは、国内外の計量経済学研究者を招聘し、計量経済理論や実証分析に関する研究報告をお願いし、議論を通じて相互に理解を深めると共に、新たな研究テーマを模索する場を提供します。計量経済学に興味をもつ研究者、ポスドク、大学院、学部学生の皆さんのご参加を歓迎します。

カテゴリ
日時
タイトル
報告者/場所
詳細
2025/10/08 (水)
16:45〜18:15
TBA
Fang Han (University of Washington)
第一共同研究室(4F 北側)
2025/08/26 (火)
16:45〜18:15
TBA
Kenichi Shimizu (University of Alberta)
第一共同研究室(4F 北側)
2025/08/06 (水)
16:45〜18:15
TBA
Yuya Sasaki (Vanderbilt University)
第一共同研究室(4F 北側)
2025/07/30 (水)
16:45〜18:15
TBA
海道宏明(ボストン大学)
第一共同研究室(4F 北側)
2025/07/02 (水)
16:45〜18:15
TBA
小池 祐太(東京大学)
第一共同研究室(4F 北側)
2025/06/04 (水)
16:45〜18:15
Yusuke Narita (Yale University)
第一共同研究室(4F 北側)
2025/05/30 (金)
11:00〜12:30
[応用ミクロ経済学セミナーと共催]
Stochastic Compliance and Identification of LATE (with Hidehiko Ichimura)
Juan Pantano (University of Hong Kong)
本館1階 106 会議室
2025/05/28 (水)
16:45〜18:15
Causal inference with auxiliary observations
太田 悠太(慶應義塾大学)
第一共同研究室(4F 北側)

要旨: In the evaluation of social programs, it is often difficult to conduct randomized controlled experiments due to non-compliance; therefore the local average treatment effect (LATE) is commonly applied. However, the LATE identifies the average treatment effect only for a subpopulation known as compliers and requires the monotonicity assumption. Given these limitations of the LATE, this paper proposes a nonparametric strategy to identify the causal effects for larger populations (such as the ATT and ATE) and to remove the monotonicity assumption in the cases of non-compliance. Our strategy utilizes two types of auxiliary observations, one is an outcome before assignment and the other is a treatment before assignment. These observations do not require specially designed experiments, and are likely to be observed in baseline surveys of the standard experiment or panel data. We show the results for the random assignment and those of multiply robust representations in the case where the random assignment is violated. We then present details of the GMM estimation and testing methods which utilize over-identified restrictions. The proposed strategy is illustrated by empirical examples which revisit the studies by Thornton (2008), Gerber et al. (2009), and Beam (2016), as well as the data set from the Oregon Health Insurance Experiment and that from an experimental data on marketing in a private sector.

2025/03/19 (水)
14:15〜17:20
Workshop on Recent Developments in Econometric Theory and Its Applications 2024
第一共同研究室(4F北側)
2025/02/19 (水)
16:45〜18:15
Constrained Classification and Policy Learning
坂口 翔政(東京大学)
第一共同研究室(4F 北側)

セミナーに関するお問い合わせは、下記連絡先までお願いします。
電子メール:terasawa(at)kier(dot)kyoto-u(dot)ac(dot)jp (秘書・寺沢)

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