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Empirical econometric modelling of food consumption using a new informational complexity approach
Author(s) -
Bearse Peter M.,
Bozdogan Hamparsum,
Schlottmann Alan M.
Publication year - 1997
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/(sici)1099-1255(199709/10)12:5<563::aid-jae453>3.0.co;2-v
Subject(s) - pooling , autoregressive model , econometrics , distributed lag , consumption (sociology) , econometric model , homogeneity (statistics) , lag , food consumption , economics , computer science , artificial intelligence , machine learning , sociology , agricultural economics , computer network , social science
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. © 1997 John Wiley & Sons, Ltd.