| 日時 | 2025年11月28日(金) 17:10-18:40 |
|---|---|
| 場所 | 西キャンパス本館34番教室 |
| 講演者1: | 澤田 真行(一橋大学・経研) 演題: A finite sample test for the continuity of a density function at a point 概要: We consider a finite sample density test for the continuity of a density function at a point. Specifically, we derive the worst-case density function that achieves the worst-case rejection probability under a Lipschitz class so that we may control the size of the test in a finite sample. We extend our analysis to consider non-increasing and Lipschitz continuous densities on [0,1]. We apply our procedure to a p-hacking detection problem using a dataset of tests reported in top Economics publications. |
| 講演者2: | 岡野 遼(一橋大学・経済学研究科特任研究員) 演題: Functional Synthetic Control Methods for Metric Space-Valued Outcomes 概要: The synthetic control method (SCM) is a widely used tool for evaluating the causal effects of policy changes in panel data settings. Recently, several studies have extended this framework to accommodate complex outcomes that take values in metric spaces, such as distributions, functions, networks, correlation matrices, and compositional data. However, theoretical guarantees for estimation and inference within these extended frameworks remain underdeveloped. In this study, we propose a novel extension of the SCM for metric space-valued outcomes. To address challenges arising from the lack of linear structure in general metric spaces, we leverage isometric embeddings of metric spaces into Hilbert spaces. Building on this approach, we develop estimators for missing potential outcomes, derive their finite-sample error bounds, and construct prediction sets for causal effects. The proposed methods are demonstrated through simulations and empirical applications. |
| 言語 | 日本語 |
| 幹事 | 本田敏雄 [経済学研究科] |
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