Generalized Additive Models for Nonmarket Valuation Via Revealed or Stated Preference Methods

37 Pages Posted: 24 Mar 2010

See all articles by Silvia Ferrini

Silvia Ferrini

University of Siena

Carlo Fezzi

University of East Anglia (UEA) - School of Environmental Sciences

Date Written: January 10, 2010

Abstract

This paper proposes a flexible modelling approach for either stated or revealed preference nonmarket valuation studies based on Generalized Additive Models (GAMs). GAMs encompass non-linear relationships via smooth functions, thereby avoiding imposing restrictive parametric specifications, which are rarely supported by economic theory. The smoothing parameters, which regulate the overall flexibility and the degree of non-linearity of the model, are directly estimated from the data using the penalized likelihood approach introduced by Wood (2000). Theoretically consistent WTP measures are derived using relatively simple modification of the standard algorithms proposed for parametric models. Two case studies, a contingent valuation and a travel cost application, illustrate the practical advantages of GAMs with respect to the conventional modelling techniques which dominate the literature.

Keywords: Nonmarket Valuation, Travel Cost, Contingent Valuation, Flexible Functional Form, Smooth Functions, Generalized Additive Models, Penalized Likelihood

JEL Classification: Q26, C14, C35

Suggested Citation

Ferrini, Silvia and Fezzi, Carlo, Generalized Additive Models for Nonmarket Valuation Via Revealed or Stated Preference Methods (January 10, 2010). Available at SSRN: https://ssrn.com/abstract=1571267 or http://dx.doi.org/10.2139/ssrn.1571267

Silvia Ferrini

University of Siena ( email )

Via Banchi di Sotto, 55
Siena
Italy

Carlo Fezzi (Contact Author)

University of East Anglia (UEA) - School of Environmental Sciences ( email )

Norwich, Norfolk
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
120
Abstract Views
1,104
Rank
472,516
PlumX Metrics