Choice Between Disaggregate and Aggregate Specifications Estimated by Instrumental Variables Methods
提出基于预测误差的准则，用于在工具变量法估计的分解模型与加总模型之间做选择，并推导加总偏差检验。应用于英国就业需求分析，发现按40个行业分解的模型比加总模型更好地预测总就业，且加总模型对长期工资和产出弹性的估计存在显著偏差。
A choice criterion is proposed for discriminating between disaggregate and aggregate models estimated by the instrumental variables method. The criterion, based on prediction errors, represents a generalization of criteria developed in the context of classical regression models. The article also derives general tests for aggregation bias in the instrumental variables context The criterion and the tests are applied in an analysis of U.K. employment demand. It is shown that a model disaggregated by 40 industries predicts aggregate employment better than an aggregate model and that significant biases exist in estimates of the long-run wage and output elasticities obtained from the aggregate model.