Model Selection and Forecasting Ability of Theory‐Constrained Food Demand Systems
利用1923-1992年美国人均食品消费数据，比较三种需求系统的预测能力，发现施加消费者理论约束的模型预测更优，其中双对数需求系统表现最佳。
Abstract Out‐of‐sample forecasting of annual U.S. per capita food consumption, applying data from 1923 to 1992, is used as a basis for model selection among the absolute price Rotterdam model, a first‐differenced linear approximate almost ideal demand system (FDLA/ALIDS) model, and a first‐differenced double‐log demand system. Conditional‐on‐price consumption forecasts derived from elasticities are determined to be superior to direct statistical model forecasts. Models with consumer theory imposed through parametric restrictions provide better forecasts than models with little theory‐imposition. For these data, a double‐log demand system is a superior forecaster to the Rotterdam model, which is superior to the FDLA/ALIDS model.