Macro Models Based on Aggregation of Micro Behavior Using Models of Changes in the Distribution of Individuals

大量的应用计量经济学分析使用汇总数据 (from人口普查局、劳工统计局等）进行测试， 应用经济中个人行为的理论。 为 例如，研究了许多需求方程的估计系统， 消费品的种类是以微观经济理论为基础的 描述个人优化行为，但使用消费者 整个国家的支出数据，从而隐含地处理 聚合的数据就像是由一个人生成的一样。 在 在类似的情况下，从理论上推导出企业的产量， 基于利润最大化的原则。 估计 然而，企业的生产函数通常是使用基于数据的 整个行业或国家。 假设一个消费者或公司代表 整个经济是一个方便的经济，因为它允许应用 以及用可用的综合数据检验微观经济理论， 它避免了聚集和集体的棘手问题， 选择 不幸的是，没有理由从经济上 理论假设，国家汇总数据，这是结果， 加上数百万不同个体的经济活动， 将类似于单个公司或消费者的经济活动。 的 因此，研究人员必须假设一些一般参数的聚合 数据，以揭示潜在的优化行为。 过去 理论工作已经表明，几乎不需要假定结构 根据汇总数据推断总需求的存在，或 供应功能，这些限制只需要放在 汇总数据的统计分布。 因此， 有效地限制消费支出的分配， 不随时间改变或以特定的方式改变。 等 分配假设也可以应用于生产。 教授Lewbel推导出总需求方程的经济， 考虑到对《公约》随时间变化的限制， 总支出在人口中的分配， 具体的个人需求行为。 这些总需求 方程满足效用最大化的所有条件， 个人需求方程的基础。 技术上来说，他构建了 宏观经济数据的非线性模型， 微观经济行为的异质性以及 经济中个体行为者的分配时间。 这 允许构建分布中的偏移模型 与聚集理论相一致。 Lewbel教授通过以下方式测试了聚合模型的理论有效性： 估计使用横截面或面板数据，并通过研究 在几个时间段内的分布数据。 这些方法应用于 各种国家数据集，如国民收入和生产总值， 消费者支出调查和美国消费者支出调查。S.电流 人口报告。

A great deal of applied econometric analysis uses aggregated data (from Census, the Bureau of Labor Statistics and so on) to test and apply theories about the behavior of individuals in the economy. For instance, studies estimating systems of demand equations for many types of consumer goods are based on the microeconomic theory describing individual optimizing behavior, but use consumer expenditure data of the entire country, thereby implicitly treating the aggregated data as if they were generated by a single person. In a similar circumstance the output of a firm is theoretically derived based on the principle of profit maximization. Estimation of production functions of firms, however, is often done using data based on an entire industry or country. The assumption of a single consumer or firm being a representative of the entire economy is a convenient one, in that it allows application and testing of microeconomic theories with available aggregate data, and it avoids intractable problems of aggregation and collective choice. Unfortunately, there is no reason deriving from economic theory to assume that national aggregate data, which is the result of adding the economic activities of millions of diverse individuals, will resemble the economic activity of a single firm or consumer. The researcher must thus assume some general parameters for the aggregated data in order to uncover the underlying optimizing behavior. Past theoretical work has shown that very little structure need be assumed on the aggregated data to infer the existence of aggregate demand or supply functions, and that those restrictions need be placed only on the statistical distribution of the aggregate data. Thus one can validly restrict the distribution of consumer expenditures to remain unchanged through time or to change in general specified ways. Such distributional assumptions can also be applied to production. Professor Lewbel derives aggregate demand equations for the economy by considering the restrictions on the changes over time in the distribution of total expenditures in the population, and combining them with specific individual demand behavior. These aggregate demand equations satisfy all the conditions of utility maximization on which individual demand equations are based. Technically, he constructs nonlinear models of macroeconomic data based on explicit models of both heterogeneous microeconomic behavior and of the evolution over time of the distribution of individual agents in the economy. This allows for the construction of models of shifts in the distributions through time that are compatible with aggregation theory. Professor Lewbel tests the theoretical validity of aggregate models by estimation using cross sectional or panel data, and by studying the distribution data in several time periods. These methods are applied to various national data sets like the National Income and Product Accounts, the Consumer Expenditure Survey, and the U. S. Current Population Reports.