Econometric Analysis of Aggregation in the Context of Linear Prediction Models
线性预测模型中聚合问题的计量分析AI 解读 ↓
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线性预测模型中聚合问题的计量分析
- 研究问题
- 预测总量变量时应采用宏观聚合方程还是微观分解方程，如何将微观误差的同期相关与不同参数限制纳入比较，并检验“完全聚合”是否成立？
- 方法与思路
- 作者推广 Grunfeld–Griliches 预测准则，允许微观方程误差同期相关与不同参数限制，并提出完全聚合检验，应用于英国四十个行业的就业需求方程。
- 材料陈述的贡献
- 摘要称，制造业与全行业均拒绝完全聚合；预测准则在制造业略偏向聚合方程，在全行业明显偏向分解方程，显示预测比较结果随聚合层级而异。
- 阅读关注点
- 核对预测准则的定义与协方差处理、完全聚合检验中系数相等和构成稳定两种路径，以及动态规格、参数限制对行业比较结果的影响。
- 阅读边界
- 摘要未说明样本期、预测评价是否使用样本外数据及检验条件；英国行业示例中的偏好不能直接推广为聚合或分解模型的一般优势。
原文摘要
This paper deals with the problem of aggregation where the focus of the analysis is whether to predict aggregate variables using macro or micro equations. The GrunfeldGriliches prediction criterion for choosing between aggregate and disaggregate equations is generalized to allow for contemporaneous covariances between the disturbances of micro equations and the possibility of different parametric restrictions on the equations of the disaggregate model. A new test is proposed of the hypothesis of 'perfect aggregation' which tests the validity of aggregation either through coefficient equality or through the stability over time of the composition of the regressors across the micro units. The tools developed in the paper are then applied to employment demand functions for the UK economy disaggregated by 40 industries. Firstly a set of unrestricted log-linear dynamic specifications are estimated for the disaggregate equations and then linear parameter restrictions are imposed as appropriate. Corresponding unrestricted and restricted aggregate equations are estimated. Two different levels of aggregation are considered: aggregation over the 23 manufacturing industries and aggregation over all 40 industries of the economy. In both cases the hypothesis of perfect aggregation is firmly rejected. For the manufacturing industries the prediction criterion marginally favors the aggregate equation but over all industries the disaggregated equations are strongly preferred.