English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Naive maximum likelihood estimation of binary logit models with fixed effectsleads to unreliable inference due to the incidental parameter problem. We studythe case of three-dimensional panel data, where the model includes three setsof additive and overlapping unobserved effects. This encompasses models fornetwork panel data, where senders and receivers maintain bilateralrelationships over time, and fixed effects account for unobserved heterogeneityat the sender-time, receiver-time, and sender-receiver levels. In an asymptoticframework, where all three panel dimensions grow large at constant relativerates, we characterize the leading bias of the naive estimator. The inferenceproblem we identify is particularly severe, as it is not possible to balancethe order of the bias and the standard deviation. As a consequence, the naiveestimator has a degenerating asymptotic distribution, which exacerbates theinference problem relative to other fixed effects estimators studied in theliterature. To resolve the inference problem, we derive explicit expressions todebias the fixed effects estimator.

Debiased Fixed Effects Estimation of Binary Logit Models with Three-Dimensional Panel Data