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Empirical Econometric Modelling of Food Consumption Using a New Informational Complexity Approach

Peter M. Bearse, Hamparsum Bozdogan and Alan M. Schlottmann
Journal of Applied Econometrics
Vol. 12, No. 5, Special Issue: The Experiment in Applied Econometrics (Sep. - Oct., 1997), pp. 563-586
Published by: Wiley
Stable URL: http://www.jstor.org/stable/2285132
Page Count: 24
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Empirical Econometric Modelling of Food Consumption Using a New Informational Complexity Approach
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Abstract

This paper is concerned with empirical econometric modelling of food consumption in the USA and the Netherlands. Using autoregressive distributed lag models (ADLs) selected via the Informational Complexity (ICOMP) criterion, we study the relationship between food consumption and income. Whether food consumption obeys the homogeneity postulate is tested using information criteria. Using information-theoretic techniques, we identify the optimal information set and lag order for a Vector Autoregressive (VAR) forecast of food consumption in the Netherlands. We demonstrate how multisample cluster analysis, a combinatorial grouping of samples or data matrices, can be used to determine when the pooling of data sets is appropriate, and how ICOMP can be used in conjunction with the Genetic Algorithm (GA) to determine the optimal predictors in the celebrated seemingly unrelated regressions (SUR) model framework.

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