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固定效应模型中的异方差检验AI 解读

We derive tests for heteroskedasticity after fixed effects estimation of linear panel models. The asymptotic results are based on a ‘large N–fixed T’ framework, where the incidental parameters problem is bypassed by utilizing a (pseudo) likelihood function conditional on the sufficient statistic for these parameters. A simple ‘studentization’ produces distribution free tests that can easily be implemented using an artificial regression based on residuals after fixed effects…

单向误差分量模型中异方差的稳健检验AI 解读

This paper constructs tests for heteroskedasticity in one-way error components models, in line with Baltagi et al. [Baltagi, B.H., Bresson, G., Pirotte, A., 2006. Joint LM test for homoskedasticity in a one-way error component model. Journal of Econometrics 134, 401-417]. Our tests have two additional robustness properties. First, standard tests for heteroskedasticity in the individual component are shown to be negatively affected by heteroskedasticity in the remainder…