Noise-adjusted turnover in estimated networks
Abstract
Economic networks are often estimated separately over two periods, and changes in their edge sets are interpreted as structural rewiring. Since both networks are estimated, observed turnover also reflects graph-selection error. We study the two-snapshot Hamming-turnover functional under a homogeneous edge-misclassification model. With known sensitivity and specificity and conditional independence of the estimated edge indicators across periods, latent turnover admits a closed-form unbiased adjustment based only on observed turnover and the two estimated graph sizes. We then examine the effects of sparsity, calibration error and dependence across periods. When the number of true links is proportional to p, a false-positive probability of order p−1 generates expected spurious turnover of order p. An error of the same order in calibrating the false-positive probability can likewise leave order-p bias after adjustment. We also derive the bias induced by cross-period dependence and give sufficient conditions for consistency relative to network size. Numerical results illustrate the finite-sample implications.
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BibTeX
@misc{amed2026noiseadjusted,
title = {{Noise-adjusted turnover in estimated networks}},
author = {Sultan Amed and Sayantan Banerjee},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.20044},
eprint = {2609.20044},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.20044},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Noise-adjusted turnover in estimated networks
AU - Sultan Amed
AU - Sayantan Banerjee
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.20044
UR - https://arxiv.org/abs/2609.20044
AB - Economic networks are often estimated separately over two periods, and changes in their edge sets are interpreted as structural rewiring. Since both networks are estimated, observed turnover also reflects graph-selection error. We study the two-snapshot Hamming-turnover functional under a homogeneous edge-misclassification model. With known sensitivity and specificity and conditional independence of the estimated edge indicators across periods, latent turnover admits a closed-form unbiased adjustment based only on observed turnover and the two estimated graph sizes. We then examine the effects of sparsity, calibration error and dependence across periods. When the number of true links is proportional to p, a false-positive probability of order p^−1 generates expected spurious turnover of order p. An error of the same order in calibrating the false-positive probability can likewise leave order-p bias after adjustment. We also derive the bias induced by cross-period dependence and give sufficient conditions for consistency relative to network size. Numerical results illustrate the finite-sample implications.
ER -