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Model Selection for the Mixed Logit with Bayesian Estimation

Balcombe, Kelvin, Chalak, Ali, Fraser, Iain M (2009) Model Selection for the Mixed Logit with Bayesian Estimation. Journal of Environmental Economics and Management, 57 (2). pp. 226-237. ISSN 0095-0696. (doi:10.1016/j.jeem.2008.06.001) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/j.jeem.2008.06.001

Abstract

In this paper, the mixed logit (ML) using Bayesian methods was employed to examine willingness-to-pay (WTP) to consume bread produced with reduced levels of pesticides so as to ameliorate environmental quality, from data generated by a choice experiment. Model comparison used the marginal likelihood, which is preferable for Bayesian model comparison and testing. Models containing constant and random parameters for a number of distributions were considered, along with models in ‘preference space’ and ‘WTP space’ as well as those allowing for misreporting. We found: strong support for the ML estimated in WTP space; little support for fixing the price coefficient a common practice advocated and adopted in the environmental economics literature; and, weak evidence for misreporting.

Item Type: Article
DOI/Identification number: 10.1016/j.jeem.2008.06.001
Additional information: number of additional authors: 2;
Uncontrolled keywords: Mixed logit; Willingness-to-pay; Model comparison
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Stewart Brownrigg
Date Deposited: 07 Mar 2014 00:05 UTC
Last Modified: 01 Aug 2019 10:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/40233 (The current URI for this page, for reference purposes)
Fraser, Iain M: https://orcid.org/0000-0002-4689-6020
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