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Pré-Publication, Document De Travail Année : 2024

Semiparametrically Efficient Estimation of Regression Models with Spillovers

Résumé

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; Bramoull´e 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 the 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.
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Dates et versions

hal-04549707 , version 1 (17-04-2024)

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Paternité - Pas d'utilisation commerciale - Pas de modification

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  • HAL Id : hal-04549707 , version 1

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Nicolas Debarsy, Vincenzo Verardi, Catherine Vermandele. Semiparametrically Efficient Estimation of Regression Models with Spillovers. 2024. ⟨hal-04549707⟩
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