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三维面板与网络数据固定效应估计的去偏推断AI 解读

Inference for fixed effects estimators is often unreliable due to Nickell- and incidental parameter biases. While these issues are well understood for classical two-dimensional panels, little is known about three-dimensional panel structures (e.g., sender x receiver x time). We develop inferential theory for a broad class of linear and nonlinear fixed effects M-estimators in this setting, covering bipartite, directed, and undirected network panel data, multiple…

非平衡面板固定效应估计量的推断AI 解读

We develop the asymptotic theory for two-way fixed effects M-estimators in unbalanced panels, within a framework where both panel dimensions grow large at proportional rates. The selection process may be deterministic, stochastic, or a combination of the two. We require neither a missing-at-random condition nor a selection equation, only a conditional mean restriction on the outcome. The uncorrected estimators are asymptotically normal but not correctly centered due to…

三维面板二元 Logit 模型的去偏固定效应估计AI 解读

Naive maximum likelihood estimation of binary logit models with fixed effects leads to unreliable inference due to the incidental parameter problem. We study the case of three-dimensional panel data, where the model includes three sets of additive and overlapping unobserved effects. This encompasses models for network panel data, where senders and receivers maintain bilateral relationships over time, and fixed effects account for unobserved heterogeneity at the sender-time,…

三维引力模型中的潜在非平衡性AI 解读

Many panel data sets used for pseudo-poisson estimation of three-way gravity models are implicitly unbalanced because uninformative observations are redundant for the estimation. We show with real data as well as simulations that this phenomenon, which we call latent unbalancedness, amplifies the inference problem recently studied by Weidner and Zylkin (2021).