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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Inference in Linear Dyadic Data Models with Network Spillovers |
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Identification Number: |
doi:10.7910/DVN/VLMMZQ |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2023-11-01 |
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Version: |
1 |
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Bibliographic Citation: |
Canen, Nathan; Sugiura, Ko, 2023, "Replication Data for: Inference in Linear Dyadic Data Models with Network Spillovers", https://doi.org/10.7910/DVN/VLMMZQ, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication Data for: Inference in Linear Dyadic Data Models with Network Spillovers |
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Identification Number: |
doi:10.7910/DVN/VLMMZQ |
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Authoring Entity: |
Canen, Nathan (University of Houston, University of Warwick and NBER) |
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Sugiura, Ko (University of Houston) |
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Producer: |
<i>Political Analysis</i> |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Canen, Nathan |
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Depositor: |
Canen, Nathan |
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Date of Deposit: |
2023-07-10 |
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Holdings Information: |
https://doi.org/10.7910/DVN/VLMMZQ |
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Study Scope |
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Keywords: |
Social Sciences |
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Abstract: |
Abstract: 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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Forthcoming, Political Analysis |
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Bibliographic Citation: |
Forthcoming, Political Analysis |
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Label: |
Canen & Sugiura - Replication Package, Oct 2023.zip |
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This .zip file contains all files for replicating the results in our paper. After extraction, you will find a ReadMe file, together with subfolders to reproduce: (i) Monte Carlo Simulations and (ii) Empirical Application results. All files are carefully explained in their respective ReadMe files. This includes the packages needed to run the files. To replicate the results, please preserve the folder structure of the zip file. The Empirical Application uses Python. For further instructions on installing the necessary packages, please see the "Python_PackageInstallation.txt" file within that subfolder. |
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Notes: |
application/zip |