摘要：We study testing for peer effects in linear panel models with individual and time fixed effects when the interaction network is unspecified. We propose a quadratic-form test based on the Gram matrix of residualized peer covariates. A key difficulty is that fixed-effects removal and nuisance estimation destroy the centering of a conventional leave-diagonal-out quadratic form under conditional heteroskedasticity. We introduce a leverage adjustment that restores exact conditional centering and combine it with a residual-based variance estimator. The resulting studentized statistic is asymptotically standard normal as both panel dimensions diverge, without imposing any restriction on their relative growth rates. Under additional conditions, we establish consistency against alternatives. The null theory is further extended to covariates that are weakly dependent over time; the same studentization remains valid without an additional long-run variance estimator. Monte Carlo experiments examine the finite-sample size and power of the proposed test across alternative panel dimensions and forms of heteroskedasticity. An empirical application illustrates its use in testing for peer effects when the underlying interaction network is unavailable.

 

简介：郭旭，现任北京师范大学统计学院教授，博士生导师。曾荣获北京师范大学第十一届“最受本科生欢迎的十佳教师”，北京师范大学第十八届“青教赛”一等奖和北京市第十三届“青教赛”三等奖。目前主要关注高维回归模型中的假设检验问题也对基于机器学习算法的统计推断感兴趣，有多篇文章发表在统计学和计量经济学国际顶尖期刊包括JRSSB, JASA, Biometrika, JOE和JMLR，担任统计学国际知名期刊JMVA副主编。