Semiparametrically Efficient Estimation of Linear Regression Models with Spillovers

Nicolas Debarsy, Vincenzo Verardi and Catherine Vermandele
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Vincenzo Verardi: Louvain Research Institute in Management and Organizations (LouRIM)
Catherine Vermandele: LMTD - Laboratoire de méthodologie du traitement des données - Université Libre de Bruxelles, Brussels, Belgium

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Abstract: Linear regression models with spillover effects generally cannot be estimated using ordinary least squares given the simultaneity that results from interactions among individuals. Instead, they are fitted using two-stage least squares (Kelejian and Prucha, 1998; Bramoullé et al., 2009), generalized method of moments (Liu et al., 2010), (quasi-) maximum likelihood typically under the normality assumption (Lee, 2004) or adaptive estimation (Robinson, 2010). In this article, we propose a semiparametrically efficient estimator, based on the Local 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 errors' distribution as a distributional assumption. Monte Carlo simulations show that the suggested estimator performs well in comparison to competing estimators. A trade regression from Behrens et al. ( 2012) is used to illustrate how empirical findings might greatly change when the Gaussian distribution is not imposed.

Keywords: Spillovers; Efficiency; Local Asymptotic Normality; Semiparametric estimation (search for similar items in EconPapers)
Date: 2024-12-12
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