9 citations · 41 across the 18 of their papers we have counts for
18 papers
Refined Cluster Robust Inference
Bulat Gafarov, Takuya Ura
It has become standard for empirical studies to conduct inference robust to cluster dependence and heterogeneity. With a small number of clusters, the normal approximation for the…
Policy Relevant Treatment Effects with Multidimensional Unobserved Heterogeneity
Takuya Ura, Lina Zhang
This paper provides a unified framework for bounding policy relevant treatment effects using instrumental variables. In this framework, the treatment selection may depend on multid…
Doubly Robust Estimators with Weak Overlap
Yukun Ma, Pedro H. C. Sant'Anna, Yuya Sasaki +1
Doubly robust (DR) estimators guard against model misspecification but remain sensitive to weak covariate overlap. We show that trimming propensity scores reduces variance but elim…
Faster estimation of dynamic discrete choice models using index invertibility
Jackson Bunting, Takuya Ura
Many estimators of dynamic discrete choice models with persistent unobserved heterogeneity have desirable statistical properties but are computationally intensive. In this paper we…
Slow Movers in Panel Data
Yuya Sasaki, Takuya Ura
Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations). In the presence of many slow movers, conventional economet…
Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
Harold D. Chiang, Kengo Kato, Yuya Sasaki +1
We develop a novel method of constructing confidence bands for nonparametric regression functions under shape constraints. This method can be implemented via a linear programming,…
Welfare Analysis via Marginal Treatment Effects
Yuya Sasaki, Takuya Ura
Consider a causal structure with endogeneity (i.e., unobserved confoundedness) in empirical data, where an instrumental variable is available. In this setting, we show that the mea…
Testing homogeneity in dynamic discrete games in finite samples
Federico A. Bugni, Jackson Bunting, Takuya Ura
The literature on dynamic discrete games often assumes that the conditional choice probabilities and the state transition probabilities are homogeneous across markets and over time…
Unconditional Quantile Regression with High Dimensional Data
Yuya Sasaki, Takuya Ura, Yichong Zhang
This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the un…
Finite Sample Inference for the Maximum Score Estimand
Adam M. Rosen, Takuya Ura
We provide a finite sample inference method for the structural parameters of a semiparametric binary response model under a conditional median restriction originally studied by Man…
Inference based on Kotlarski's Identity
Kengo Kato, Yuya Sasaki, Takuya Ura
Kotlarski's identity has been widely used in applied economic research. However, how to conduct inference based on this popular identification approach has been an open question fo…
Estimation and Inference for Policy Relevant Treatment Effects
Yuya Sasaki, Takuya Ura
The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy. Estimation of the PRTE involves estimation…
Identification and Inference of Network Formation Games with Misclassified Links
Luis E. Candelaria, Takuya Ura
This paper considers a network formation model when links are potentially misclassified. We focus on a game-theoretical model of strategic network formation with incomplete informa…
Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias
Yuya Sasaki, Takuya Ura
Empirical researchers often trim observations with small denominator A when they estimate moments of the form E[B/A]. Large trimming is a common practice to mitigate variance, but…
Non-separable Models with High-dimensional Data
Liangjun Su, Takuya Ura, Yichong Zhang
This paper studies non-separable models with a continuous treatment when the dimension of the control variables is high and potentially larger than the effective sample size. We pr…
Instrumental Variable Quantile Regression with Misclassification
Takuya Ura
This paper considers the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, 2013) with a binary endogenous treatment. It offers two identification resu…
Inference in Dynamic Discrete Choice Problems under Local Misspecification
Federico A. Bugni, Takuya Ura
Single-agent dynamic discrete choice models are typically estimated using heavily parametrized econometric frameworks, making them susceptible to model misspecification. This paper…
Heterogeneous Treatment Effects with Mismeasured Endogenous Treatment
Takuya Ura
This paper studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treatment is mismeasured and endoge…