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Associate and Professor of Econometrics
Expertise: nonparametric and semiparametric methods, microeconometrics
01 July 2024
Daisuke Kurisu, Hans-Georg Mueller, Taisuke Otsu and Yidong Zhou
Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular tool to achieve this. Causal inference ... Read more...
06 February 2024
Daisuke Kurisu and Taisuke Otsu
There has been growing interest in statistical analysis on random objects taking values in a non-Euclidean metric space. One important class of such objects consists of data on manifolds. This article is concerned with i... Read more...
25 October 2023
Daisuke Kurisu, Taisuke Otsu and Mengshan Xu
Functional data and their analysis have become increasingly popular in various fields of data sciences. This paper considers estimation and inference of the average treatment effect under unconfoundedness when the covari... Read more...
26 September 2023
Hao Dong, Taisuke Otsu and Luke Taylor
The convergence rate of an estimator can vary when applied to datasets from differ- ent populations. As the population is unknown in practice, so is the corresponding convergence rate. In this paper, we introduce a metho... Read more...
07 August 2023
Yukitoshi Matsushita and Taisuke Otsu
This article develops a concept of nonparametric likelihood for network data based on network moments, and proposes general inference methods by adapting the theory of jackknife empirical likelihood. Our methodology can ... Read more...
21 July 2023
Petersen and Müller (2019) generalized the notion of regression analysis to non-Euclidean response objects. Meanwhile, in the conventional regression analysis, model averaging has long history and is widely applied in st... Read more...
09 February 2023
Harold D Chiang, Yukitoshi Matsushita and Taisuke Otsu
This paper is concerned with estimation and inference on average treatment effects in randomized controlled trials when researchers observe potentially many covariates. By em- ploying Neyman’s (1923) finite population pe... Read more...
31 October 2022
Yoichi Arai, Taisuke Otsu and Myung Hwan Seo
This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis. In particular, we propose estimation and inference methods for the RDD models with covariate sele... Read more...
27 October 2022
Yoichi Arai, Taisuke Otsu and Mengshan Xu
The generalized least square (GLS) is one of the most basic tools in regression analyses. A major issue in implementing the GLS is estimation of the conditional variance function of the error term, which typically requir... Read more...
11 October 2022
Sreevidya Ayyar, Yukitoshi Matsushita and Taisuke Otsu
This paper extends validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003) to instrumental variable regression models with unknown error variance and many weak instruments. In this setting, we... Read more...
04 December 2008
Taisuke Otsu (Yale University)
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