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.
Origine : Fichiers produits par l'(les) auteur(s)
Licence : CC BY NC ND - Paternité - Pas d'utilisation commerciale - Pas de modification
Licence : CC BY NC ND - Paternité - Pas d'utilisation commerciale - Pas de modification