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Food consumption dynamics: a nonparametric approach using maximum entropy estimation
Author(s) -
Stokes J. R.,
Frechette D. L.
Publication year - 2006
Publication title -
agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.29
H-Index - 82
eISSN - 1574-0862
pISSN - 0169-5150
DOI - 10.1111/j.1574-0864.2006.00125.x
Subject(s) - principle of maximum entropy , econometrics , nonparametric statistics , markov chain , consumption (sociology) , parametric statistics , sample (material) , observable , economics , mixture model , mathematics , statistics , sociology , social science , chemistry , physics , chromatography , quantum mechanics
We model dynamic consumer choice in a stochastic optimal control framework and show conditions under which observable market share data possess the Markov property. Using 30 years of annual aggregate milk consumption data differentiated by fat content, maximum entropy is used to estimate nonstationary transition probabilities showing how consumer tastes and preferences have changed over time. The maximum entropy approach allows for the estimation of a 4 × 4 transition probability matrix for each year of the sample. Results suggest that skim milk was an absorbing state over most of the sample but that the trend toward skim milk has decelerated and possibly reversed itself since 1998. Our approach provides a useful complement to existing parametric approaches to demand analysis when data are limited or the problem is ill‐posed.