Testing for Peer Effects in Heteroskedastic Fixed-Effects Panels

Abstract

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 evaluate the finite-sample size and power of the proposed test across different settings and demonstrate its favorable performance relative to the existing benchmark test. Applications to state tax competition and growth spillovers among OECD countries illustrate the practical relevance in testing for peer effects without specifying the interaction network.