The Use of Cross-Section Data to Characterize Macro Functions
作者:
ThomasM. Stoker,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1982)
卷期:
Volume 77,
issue 378
页码: 369-380
ISSN:0162-1459
年代: 1982
DOI:10.1080/01621459.1982.10477819
出版商: Taylor & Francis Group
关键词: Cross-section least squares regression;Aggregation theory;Asymptotic sufficiency;Linear aggregation;Sufficient statistics;Exponential family
数据来源: Taylor
摘要:
This article investigates the use of individual cross-section data to describe macro functions. Necessary and sufficient conditions (denoted by AS) are found for ordinary least squares (OLS) slope coefficients from a cross section to consistently estimate the first derivatives of the macro function. AS embodies both sets of aggregation assumptions known; linear aggregation and sufficient statistics, and thus represents generalized aggregation conditions. A methodology is given for estimating second-order derivatives of the macro function from cross-section data for distributions of the exponential family, which extends to higher-order derivatives. Finally, a general test of linear aggregation schemes is presented.
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