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.

翻译：本文针对包含个体固定效应的真实非平衡动态网络面板数据模型，提出了一种拟极大似然估计方法。我们构建的模型能够同时容纳同期与滞后的网络溢出效应、时间依赖性，以及当单元首次出现在面板中时被激活的上市效应。我们证明了当 $N$ 和 $T$ 均趋于无穷大时该拟极大似然估计量的一致性，推导了其渐近分布，并识别出当 $N$ 相对于 $T$ 渐近较大时由冗余参数引起的渐近偏差。基于该渐近偏差表达式，我们提出了一种偏差校正估计量，该估计量在适当的正则条件下是渐近无偏且服从正态分布的。蒙特卡洛实验从偏差、均方根误差、覆盖概率以及估计量的正态性等多个标准，检验了偏差校正估计量在有限样本下的表现。对来自新西兰和纽约市的Airbnb房源数据的实证应用揭示了空间与时间价格传导中存在的区域特定模式，从而说明了在动态网络环境中对真实非平衡性进行建模的重要性。