Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand SystemsKehlbacher, A., Srinivasan, C. ORCID: https://orcid.org/0000-0003-2537-7675, McCloy, R. ORCID: https://orcid.org/0000-0003-2333-9640 and Tiffin, R. (2020) Modelling preference heterogeneity using a Bayesian finite mixture of Almost Ideal Demand Systems. European Review of Agricultural Economics, 47 (3). pp. 933-970. ISSN 0165-1587
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1093/erae/jbz002 Abstract/SummaryDemand studies often use observable characteristics to proxy preference heterogeneity. It is likely, however, that some households with the same observable characteristics have quite different preferences. An alternative approach is to use a Gaussian mixture of Almost Ideal Demand Systems to capture the heterogeneity. We show how to estimate this with censored for 5 food categories using Bayesian inference. Using model outputs we infer four different preference classes; how distinct these classes are from one another and which food categories are driving the segmentation process.
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