Semiparametrically Efficient Estimation of Regression Models with Spillovers

Résumé 0

Regression models with spillover effects generally cannot be estimated using ordinaryleast squares given the simultaneity that results from interactions among individuals.Instead, they are fitted using two-stage least squares (Kelejian and Prucha,1998; Bramoull´e et al., 2009), generalized method of moments (Liu et al., 2010), (quasi-)maximum likelihood typically under the normality assumption (Lee, 2004) or adaptiveestimation (Robinson, 2010).In this article, we propose a semiparametrically efficient estimator, based on theLocal Asymptotic Normality theory of Le Cam (1960) and on the work of Hallin et al.(2006, 2008) on residuals ranks-and-signs, that only requires strong unimodality of theerrors’ distribution as a distributional assumption. Monte Carlo simulations show thatthe suggested estimator performs well in comparison to competing estimators. A traderegression from Behrens et al. (2012) is used to illustrate how empirical findings mightgreatly change when the Gaussian distribution is not imposed.