Ko Sugiura
Biographic Data
| ID | 6610137 |
|---|---|
| NAME | Ko Sugiura |
| GIVEN NAMES | Ko |
| FAMILY NAME | Sugiura |
| SIGNATURE | SUGIURA K |
| AFFILIATIONS | University of Houston |
| VERIFIED | No |
| TOTAL WORKS | 1 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 1 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Inference in Linear Dyadic Data Models with Network Spillovers
When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares, and conduct inference using “dyadic-robust” variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because in…
No prominent works on this page.
Inference in Linear Dyadic Data Models with Network Spillovers
When using dyadic data (i.e., data indexed by pairs of units), researchers typically assume a linear model, estimate it using Ordinary Least Squares, and conduct inference using “dyadic-robust” variance estimators. The latter assumes that dyads are uncorrelated if they do not share a common unit (e.g., if the same individual is not present in both pairs of data). We show that this assumption does not hold in many empirical applications because in…
Artificial Intelligence (1 work) · Computer Science (1 work) · Econometrics (1 work) · Economics (1 work) · Electoral Systems and Political Participation (1 work) · Estimator (1 work) · Inference (1 work) · Local Government Finance and Decentralization (1 work) · Mathematics (1 work) · Ordinary least squares (1 work)