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对序列依赖稳健的横截面相关自助法检验AI 解读

Summary We propose a novel bootstrap-based test for cross-sectional dependence (CD) in panel models, maintaining robustness against serial dependence. While serial dependence is common in panel data, existing tests often assume serial independence. Our cluster wild bootstrap CD test procedure mirrors Pesaran’s original CD test and is very simple to implement. This procedure preserves serial dependence while testing for cross-sectional independence. Theoretical validity is…

使用组别交互固定效应多层回归模型进行政策分析AI 解读

The use of multilevel regression models is prevalent in policy analysis to estimate the effect of group level policies on individual outcomes. In order to allow for the time varying effect of group heterogeneity and the group specific impact of time effects, we propose a group interactive fixed effects approach that employs interaction terms of group factor loadings and common factors in this model. For this approach, we consider the least squares estimator and associated…

非线性面板固定效应估计的 Bootstrap 与 k 步 Bootstrap 偏差修正AI 解读

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n , the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k -step bootstrap to correct the bias. We establish the asymptotic…

固定效应线性面板模型中对异方差与时空依赖稳健的推断AI 解读

This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator that nests existing estimators as special cases. Our estimator improves upon existing estimators in terms of robustness, efficiency, and adaptiveness. For distributional approximations, we considered two types of asymptotics: the increasing-smoothing asymptotics…