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

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  • Bearse, Peter M
  • Bozdogan, Hamparsum
  • Schlottmann, Alan M

Abstract

This paper is concerned with empirical econometric modeling 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.

Suggested Citation

  • Bearse, Peter M & Bozdogan, Hamparsum & Schlottmann, Alan M, 1997. "Empirical Econometric Modelling of Food Consumption Using a New Informational Complexity Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(5), pages 563-586, Sept.-Oct.
    • Handle: RePEc:jae:japmet:v:12:y:1997:i:5:p:563-86
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    Cited by:

    1. Susan Helper & David I. Levine & Elliot Bendoly, 2002. "Employee Involvement And Pay At Us And Canadian Auto Suppliers," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(2), pages 329-377, June.
    2. Farshid Vahid, 2000. "Clustering Regression Functions in a Panel," Econometric Society World Congress 2000 Contributed Papers 0251, Econometric Society.
    3. Ville Aalto-Setälä & Jouko Kinnunen & Katri Koistinen, 2004. "Reasons for high food prices in small market areas: The case of the Åland Islands," Agribusiness, John Wiley & Sons, Ltd., vol. 20(1), pages 17-29.
    4. Bozdogan, Hamparsum & Haughton, Dominique M. A., 1998. "Informational complexity criteria for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 28(1), pages 51-76, July.
    5. Heng Xu & Nan Zhang, 2022. "From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research," Management Science, INFORMS, vol. 68(10), pages 7383-7401, October.
    6. Jan Hanousek & Jaromir Antoch, 2000. "Model Selection and Simplification Using Lattices," CERGE-EI Working Papers wp164, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    7. Damjan Pfajfar & Emiliano Santoro, 2007. "Heterogeneity, Asymmetries and Learning in InfIation Expectation Formation: An Empirical Assessment," Money Macro and Finance (MMF) Research Group Conference 2006 123, Money Macro and Finance Research Group.

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