Inference in Linear Dyadic Data Models with Network Spillovers
Bibliographic Data
| ID | 12370622 |
|---|---|
| Authors | Nathan Canen (0000-0002-6444-145X, University of Warwick, corresponding author), Ko Sugiura (University of Houston) |
| Year | 2023 |
| Volume | 32 |
| Issue | 3 |
| Pages | 1-18 |
| Publication date | 2023-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/pan.2023.40 |
| OpenAlex | W4389239606 |
| Language | EN |
| References cited | 32 |
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 indirect links may exist due to network connections, generating correlated outcomes. Hence, “dyadic-robust” estimators can be biased in such situations. We develop a consistent variance estimator for such contexts by leveraging results in network statistics. Our estimator has good finite-sample properties in simulations, while allowing for decay in spillover effects. We illustrate our message with an application to politicians’ voting behavior when they are seating neighbors in the European Parliament
Econometrics · Economics · Estimator · Inference · Ordinary least squares · Sample (material · Spillover effect · Statistics · Uncorrelated · Variance (accounting · Voting · Computer Science · Electoral Systems and Political Participation · Local Government Finance and Decentralization · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence
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| Highly cited | No |
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