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Semiparametrically Efficient Estimation of Regression Models with Spillovers

Author

Listed:
  • Nicolas Debarsy

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Vincenzo Verardi

    (UNamur - Université de Namur [Namur])

  • Catherine Vermandele

    (ULB - Université libre de Bruxelles)

Abstract

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.

Suggested Citation

  • Nicolas Debarsy & Vincenzo Verardi & Catherine Vermandele, 2024. "Semiparametrically Efficient Estimation of Regression Models with Spillovers," Working Papers hal-04549707, HAL.
  • Handle: RePEc:hal:wpaper:hal-04549707
    Note: View the original document on HAL open archive server: https://hal.science/hal-04549707
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