| By: |
Catalina M. Torres Figuerola (Centre de Recerca Econòmica (UIB · Sa Nostra));
Nick Hanley (University of Stirling);
Sergio Colombo (Agricultural Economics Department (IFAPA)) |
| Abstract: |
A range of empirical approaches to representing preference heterogeneity have
emerged in choice modelling. Researchers have been able to explore the
differences which selection of a particular approach makes to welfare measures
in a particular dataset, and indeed have been able to implement a number of
tests for which approach best fits a particular set of data. However, the
question as to the degree of error in welfare estimation from an inappropriate
choice of empirical approach has not been addressed. In this paper, we use
Monte Carlo analysis to address this question. Given the high popularity of
both the random parameter logit (RPL) and latent class models among choice
modellers, we examine the errors in welfare estimates from using the incorrect
model to account for taste heterogeneity. Our main finding is that using an
RPL specification with log-normally distributed preferences seems the best bet. |
| Keywords: |
Preference heterogeneity, welfare measurement, accuracy, efficiency, choice experiments, Monte Carlo analysis |
| Date: |
2011 |
| URL: |
http://d.repec.org/n?u=RePEc:pdm:wpaper:2011/1&r=dcm |