Empirical econometric modelling of food consumption using a new informational complexity approach
用信息复杂度准则选择自回归分布滞后模型，研究美国和荷兰的食品消费与收入关系，并检验齐次性假设，还展示了多样本聚类分析和遗传算法在模型选择中的应用。
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.