Quasi-Maximum Likelihood Estimation for a Genuinely Unbalanced Dynamic Network Panel Data Model

TL;DR

This paper introduces a quasi-maximum likelihood estimator for unbalanced dynamic network panel data models with fixed effects, accounting for spillovers, temporal dependence, and listing effects, and demonstrates its theoretical properties and practical performance.

Contribution

It develops a novel bias-corrected QMLE for unbalanced network panel data models with fixed effects, addressing incidental parameter bias and providing asymptotic properties.

Findings

The bias-corrected estimator is asymptotically unbiased and normally distributed.

Monte Carlo simulations show improved bias and RMSE performance.

Empirical analysis reveals region-specific price transmission patterns.

Abstract

This paper develops a quasi-maximum likelihood estimator for genuinely unbalanced dynamic network panel data models with individual fixed effects. We propose a model that accommodates contemporaneous and lagged network spillovers, temporal dependence, and a listing effect that activates upon a unit's first appearance in the panel. We establish the consistency of the QMLE as both $N$ and $T$ go to infinity, derive its asymptotic distribution, and identify an asymptotic bias arising from incidental parameters when $N$ is asymptotically large relative to $T$. Based on the asymptotic bias expression, we propose a bias-corrected estimator that is asymptotically unbiased and normally distributed under appropriate regularity conditions. Monte Carlo experiments examine the finite sample performance of the bias-corrected estimator across different criteria, including bias, RMSE, coverage probability, and the normality of the estimator. The empirical application to Airbnb listings from New Zealand and New York City reveals region-specific patterns in spatial and temporal price transmission, illustrating the importance of modeling genuine unbalancedness in dynamic network settings.