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Semiparametric estimation and testing in a model of environmental regulation with adverse selection

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  • Pascal Lavergne

    ()

  • Alban Thomas

    ()

Abstract

We consider a principal–agent model of environmental regulation with adverse selection, where firms are regulated through contracts. We show how the model allows to recover information on structural cost parameters. We use a semiparametric method to estimate consistently such parameters without specifying the distribution of the agent’s private information. We also show how to check for the specification of the econometric model, as well as auxiliary parametric assumptions, by means of specification tests based on nonparametric estimation. Results are used to discuss a selection of economic issues related to environmental regulation. Copyright Springer-Verlag 2005

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File URL: http://hdl.handle.net/10.1007/s00181-004-0225-5
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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 30 (2005)
Issue (Month): 1 (January)
Pages: 171-192

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Handle: RePEc:spr:empeco:v:30:y:2005:i:1:p:171-192

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Related research

Keywords: Semiparametric and non parametric methods; asymmetric and private information; C14; D82;

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Cited by:
  1. Xavier d'Haultfoeuille & Philippe Février, 2010. "Identification of a Class of Adverse Selection Models with Contracts Variation," Working Papers 2011-27, Centre de Recherche en Economie et Statistique.
  2. Sheriff, Glenn, 2006. "Empirical Calibration of a Least-Cost Conservation Reserve Program," 2006 Annual meeting, July 23-26, Long Beach, CA 21420, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  3. Xavier d'Haultfoeuille & Philippe Février, 2011. "The Provision of Wage Incentives : A Structural Estimation Using Contracts Variation," Working Papers 2011-29, Centre de Recherche en Economie et Statistique.
  4. Sheriff, Glenn, 2009. "Implementing second-best environmental policy under adverse selection," Journal of Environmental Economics and Management, Elsevier, vol. 57(3), pages 253-268, May.

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