Soichiro Yamauchi
Biographic Data
| ID | 248574 |
|---|---|
| NAME | Soichiro Yamauchi |
| GIVEN NAMES | Soichiro |
| FAMILY NAME | Yamauchi |
| SIGNATURE | YAMAUCHI S |
| AFFILIATIONS | Harvard University |
| ORCID | 0000-0003-0554-2717 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 12 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 2 |
The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level
Debates over racial voting, and over policies to combat vote dilution, turn on the extent to which groups' voting preferences differ and vary across geography. We present the first study of racial voting patterns in every congressional district (CD) in the United States. Using large-sample surveys combined with aggregate demographic and election data, we find that national-level differences across racial groups explain 60% of the variation in dis…
Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis
Researchers of time series cross-sectional data regularly face the change-point problem, which requires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high-dimensional data and present its application in the context of the fixed-effect model. Our proposed method, …
Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs
While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation ac…
Using Lasso to Assist Imputation and Predict Child Well-being
This article documents an approach to predicting children's well-being using data from the Fragile Families and Child Wellbeing Study, which are representative of births in large U.S. cities. The authors use the least absolute shrinkage and selection operator (LASSO) to preprocess the data. They then apply the Amelia algorithm to impute missing data. Finally, they use LASSO again for prediction with the imputed data. The authors report the perfor…
The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level
Debates over racial voting, and over policies to combat vote dilution, turn on the extent to which groups' voting preferences differ and vary across geography. We present the first study of racial voting patterns in every congressional district (CD) in the United States. Using large-sample surveys combined with aggregate demographic and election data, we find that national-level differences across racial groups explain 60% of the variation in dis…
Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis
Researchers of time series cross-sectional data regularly face the change-point problem, which requires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high-dimensional data and present its application in the context of the fixed-effect model. Our proposed method, …
Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs
While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation ac…
Using Lasso to Assist Imputation and Predict Child Well-being
This article documents an approach to predicting children's well-being using data from the Fragile Families and Child Wellbeing Study, which are representative of births in large U.S. cities. The authors use the least absolute shrinkage and selection operator (LASSO) to preprocess the data. They then apply the Amelia algorithm to impute missing data. Finally, they use LASSO again for prediction with the imputed data. The authors report the perfor…
Using Lasso to Assist Imputation and Predict Child Well-being
This article documents an approach to predicting children's well-being using data from the Fragile Families and Child Wellbeing Study, which are representative of births in large U.S. cities. The authors use the least absolute shrinkage and selection operator (LASSO) to preprocess the data. They then apply the Amelia algorithm to impute missing data. Finally, they use LASSO again for prediction with the imputed data. The authors report the perfor…
Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis
Researchers of time series cross-sectional data regularly face the change-point problem, which requires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high-dimensional data and present its application in the context of the fixed-effect model. Our proposed method, …
Using Multiple Pretreatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs
While a difference-in-differences (DID) design was originally developed with one pre- and one posttreatment period, data from additional pretreatment periods are often available. How can researchers improve the DID design with such multiple pretreatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pretreatment periods: (1) assessing the parallel trends assumption, (2) improving estimation ac…
The Geography of Racially Polarized Voting: Calibrating Surveys at the District Level
Debates over racial voting, and over policies to combat vote dilution, turn on the extent to which groups' voting preferences differ and vary across geography. We present the first study of racial voting patterns in every congressional district (CD) in the United States. Using large-sample surveys combined with aggregate demographic and election data, we find that national-level differences across racial groups explain 60% of the variation in dis…
Mathematics (4 works) · Statistics (4 works) · Computer Science (3 works) · Econometrics (3 works) · Geography (2 works) · Machine learning (2 works) · Regression (2 works) · Statistical Methods and Bayesian Inference (2 works) · Advanced Causal Inference Techniques (1 works) · Artificial Intelligence (1 works)