27 citations · 35 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
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…