Title: Statistical Aggregation Analysis Characterizing Macro Functions, With Cross Section Data Classic Reprint
Publisher: Forgotten Books
Publication Date: 2018
Language: English
ISBN 10: 0484582194
ISBN 13: 9780484582193
Binding: HRD
Condition: New
Weight: 299 grams

About this title

Synopsis

Unlock the hidden links between individual behavior and macro outcomes

Discover how cross‑section data can reveal part of the true macro relation without assuming a specific micro function or distribution.

This concise work explains how ordinary least squares on cross sections can, under certain conditions, provide information about the macro function’s derivatives. It compares two major aggregation ideas—linear aggregation and sufficiency—showing how each shapes what can be learned from data. The text also outlines how cross‑section moments can be used to estimate higher‑order derivatives when the data come from an exponential family, and it offers practical tests for when linear aggregation holds.

Learn the key idea of asymptotic sufficiency and why it matters for estimating macro relations from cross sections
See how aggregation assumptions affect what OLS slope coefficients actually estimate
Understand the role of sufficient statistics and exponential family distributions in characterizing macro functions
Explore a framework for testing linear aggregation and for deriving higher‑order derivatives from cross‑section data

Ideal for readers interested in econometrics, aggregation theory, and methods that link micro behavior to macro patterns without heavy modeling assumptions.

"About this title" may belong to another edition of this title.

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