5 citations · 8 across the 12 of their papers we have counts for
14 papers
Local Gaussian copula inference with structural breaks: testing dependence predictability
Alexander Mayer, Tatsushi Oka, Dominik Wied
We propose a score test for dependence predictability in conditional copulas that is robust to temporal instabilities. Our semiparametric procedure accommodates flexible dynamics i…
Inflation Target at Risk: A Time-varying Parameter Distributional Regression
Yunyun Wang, Tatsushi Oka, Dan Zhu
Inflation exhibits state-dependent, skewed, and fat-tailed dynamics that make risk a central concern for monetary policy. Accordingly, inflation risks are distributional and cannot…
Safe Collaborative Filtering
Riku Togashi, Tatsushi Oka, Naoto Ohsaka +1
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effect…
Distributional Vector Autoregression: Eliciting Macro and Financial Dependence
Yunyun Wang, Tatsushi Oka, Dan Zhu
Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series. In this study, we…
Quantile Random-Coefficient Regression with Interactive Fixed Effects: Heterogeneous Group-Level Policy Evaluation
Ruofan Xu, Jiti Gao, Tatsushi Oka +1
We propose a quantile random-coefficient regression with interactive fixed effects to study the effects of group-level policies that are heterogeneous across individuals. Our appro…
Semiparametric Single-Index Estimation for Average Treatment Effects
Difang Huang, Jiti Gao, Tatsushi Oka
We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates miss…
Bivariate Distribution Regression with Application to Insurance Data
Yunyun Wang, Tatsushi Oka, Dan Zhu
Understanding variable dependence, particularly eliciting their statistical properties given a set of covariates, provides the mathematical foundation in practical operations manag…
A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China
Tatsushi Oka, Wei Wei, Dan Zhu
Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time…
Heterogeneous Impact of the Minimum Wage: Implications for Changes in Between- and Within-group Inequality
Tatsushi Oka, Ken Yamada
Workers who earn at or below the minimum wage in the United States are mostly either less educated, young, or female. Little is known, however, concerning the extent to which the m…
Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures
Dukpa Kim, Tatsushi Oka, Francisco Estrada +1
What transpires from recent research is that temperatures and radiative forcing seem to be characterized by a linear trend with two changes in the rate of growth. The first occurs…
Indirect Inference with a Non-Smooth Criterion Function
David T. Frazier, Tatsushi Oka, Dan Zhu
Indirect inference requires simulating realisations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model param…
Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with only Two Time Periods
Brantly Callaway, Tong Li, Tatsushi Oka
This paper shows that the Conditional Quantile Treatment Effect on the Treated can be identified using a combination of (i) a conditional Distributional Difference in Differences a…
Testing for Common Breaks in a Multiple Equations System
Tatsushi Oka, Pierre Perron
The issue addressed in this paper is that of testing for common breaks across or within equations of a multivariate system. Our framework is very general and allows integrated regr…
The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series
Heejoon Han, Oliver Linton, Tatsushi Oka +1
This paper proposes the cross-quantilogram to measure the quantile dependence between two time series. We apply it to test the hypothesis that one time series has no directional pr…