27 citations · 35 across the 18 of their papers we have counts for
21 papers
GMM under finite-population asymptotics: instrumental variables and regression adjustment
Haruo Kakehi, Yukitoshi Matsushita, Taisuke Otsu
This paper extends the finite-population asymptotic framework of Abadie et al. (2014,2020) to the generalized method of moments (GMM) estimator for moment condition models. Motivat…
Power enhancement via cross-fit variance estimation: Applications to specification, overidentification, and many-restriction testing
Keita Sunada, Yukitoshi Matsushita, Taisuke Otsu
Quadratic-form test statistics are widely used in econometrics, and their performance depends on accurate variance estimation. Conventional plug-in estimators are consistent under…
Monotone Response for Random Objects
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
Monotone treatment response (MTR), monotone treatment selection (MTS), and monotone instrumental variable (MIV) assumptions are widely used to partially identify counterfactual mea…
Lee Bounds for Random Objects
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
In applied research, Lee (2009) bounds are widely applied to bound the average treatment effect in the presence of selection bias. This paper extends the methodology of Lee bounds…
Difference-in-Differences with Interval Data
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
Difference-in-differences (DID) is one of the most popular tools used to evaluate causal effects of policy interventions. This paper extends the DID methodology to accommodate inte…
Empirical Likelihood for Random Forests and Ensembles
Harold D. Chiang, Yukitoshi Matsushita, Taisuke Otsu
We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty.…
Random sets from the perspective of metric statistics
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
Since the seminal work by Beresteanu and Molinari(2008), the random set theory and related inference methods have been widely applied in partially identified econometric models. Me…
Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Regression discontinuity designs (RDDs) are widely used for causal inference in observational studies with cutoff-based treatment assignment, primarily for Euclidean outcomes. We p…
Geodesic Synthetic Control Methods for Random Objects and Functional Data
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
We introduce a geodesic synthetic control method for causal inference with panel outcomes that are random objects in a geodesic metric space. Examples include distributions, compos…
Geodesic Difference-in-Differences
Yidong Zhou, Daisuke Kurisu, Taisuke Otsu +1
Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes…
Geodesic Causal Inference
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular…
Optimal testing in a class of nonregular models
Yuya Shimizu, Taisuke Otsu
This paper studies optimal hypothesis testing for nonregular econometric models with parameter-dependent support. We consider both one-sided and two-sided hypothesis testing and de…
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity
Yike Wang, Chris Gu, Taisuke Otsu
This paper presents a novel application of graph neural networks for modeling and estimating network heterogeneity. Network heterogeneity is characterized by variations in unit's d…
Regression adjustment in completely randomized experiments with many covariates
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper investigates estimation and inference for average treatment effects in completely randomized experiments when researchers observe potentially many covariates. Within Ney…
Conditional Likelihood Ratio Test with Many Weak Instruments
Sreevidya Ayyar, Yukitoshi Matsushita, 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 ma…
GLS under Monotone Heteroskedasticity
Yoichi Arai, Taisuke Otsu, 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 o…
Isotonic propensity score matching
Mengshan Xu, Taisuke Otsu
We propose a one-to-many matching estimator of the average treatment effect based on propensity scores estimated by isotonic regression. This approach is predicated on the assumpti…
Regression Discontinuity Design with Potentially Many Covariates
Yoichi Arai, Taisuke Otsu, 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 method…
Multiway empirical likelihood
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood s…
On Gaussian Approximation for M-Estimator
Masaaki Imaizumi, Taisuke Otsu
This study develops a non-asymptotic Gaussian approximation theory for distributions of M-estimators, which are defined as maximizers of empirical criterion functions. In existing…
Local M-estimation with Discontinuous Criterion for Dependent and Limited Observations
Myung Hwan Seo, Taisuke Otsu
This paper examines asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume pre…