Statistical Aggregation Analysis: Characterizing Macro Functions

Language: English

Published by Forgotten Books, 2024

1330285883 / 9781330285886

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Print on Demand. This book studies the use of cross-section data to describe macro functions, which are typically derived from microeconomic theories but estimated with time-series data. It introduces a new property called asymptotic sufficiency, which characterizes when cross-section data can be used to estimate the first derivatives of macro functions. The author explores the relationship between asymptotic sufficiency and aggregation assumptions and shows that it embodies both kinds of aggregation assumptions known: linear aggregation and sufficient statistics. The book presents a methodology for estimating higher-order derivatives of macro functions from cross-section data when the distribution is a member of the exponential family, which extends to higher-order derivatives. Finally, it describes a general test of linear aggregation schemes that relies only on the existence of certain population moments. This book is a valuable resource for researchers and practitioners interested in using cross-section data to characterize macro functions. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book.

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Title
Statistical Aggregation Analysis: Characterizing Macro Functions
Author
Thomas M. Stoker
Publisher
Forgotten Books
Publication year
2024
Condition
New
Book Type
print-on-demand item
Binding
Paperback
Language
English
ISBN 10
1330285883
ISBN 13
9781330285886
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Forgotten Books

London, United Kingdom

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Forgotten Books’ Classic Reprint Series utilizes the latest technology to regenerate facsimiles of historically important writings. Careful attention has been made to accurately preserve the original format of each page whilst digitally enhancing the quality of the aged text.

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