A regime-adaptive projection wild bootstrap achieves uniform validity for two-way clustered regression inference across four feasible asymptotic regimes while permitting serial and spatial dependence.
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A Neyman-orthogonal moment estimator with adjusted nonparametric fixed effects achieves root-NT asymptotic normality for common parameters in two-way heterogeneous panel models.
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Bootstrap Inference under General Two-way Clustering with Serially and Spatially Dependent Common Effects
A regime-adaptive projection wild bootstrap achieves uniform validity for two-way clustered regression inference across four feasible asymptotic regimes while permitting serial and spatial dependence.
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A Neyman-orthogonal moment estimator with adjusted nonparametric fixed effects achieves root-NT asymptotic normality for common parameters in two-way heterogeneous panel models.
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