Semiparametrically Efficient Estimation ...
Type de document :
Pré-publication ou Document de travail
URL permanente :
Titre :
Semiparametrically Efficient Estimation of Regression Models with Spillovers
Auteur(s) :
Debarsy, Nicolas [Auteur]
Lille économie management - UMR 9221 [LEM]
Verardi, Vincenzo [Auteur]
Université de Namur [Namur] [UNamur]
Vermandele, Catherine [Auteur]
Université libre de Bruxelles [ULB]
Lille économie management - UMR 9221 [LEM]
Verardi, Vincenzo [Auteur]
Université de Namur [Namur] [UNamur]
Vermandele, Catherine [Auteur]
Université libre de Bruxelles [ULB]
Date de publication :
2024-04
Mot(s)-clé(s) en anglais :
Statistical efficiency
Local Asymptotic Normality
Semiparametric estimation
Ranks and signs
Spillovers effects
Local Asymptotic Normality
Semiparametric estimation
Ranks and signs
Spillovers effects
Discipline(s) HAL :
Sciences de l'Homme et Société/Economies et finances
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Statistiques [math.ST]
Résumé en anglais : [en]
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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Collections :
Source :
Date de dépôt :
2024-04-19T02:02:32Z
Fichiers
- document
- Accès libre
- Accéder au document
- Paper_DVV.pdf
- Accès libre
- Accéder au document