The Variances of Regression Coefficient Estimates Using Aggregate Data
Language: English
Published by Forgotten Books, 2018
- Softcover
- New



Seller: Forgotten Books, London, United KingdomForgotten Books
AbeBooks seller since June 2, 2021
Condition: New
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Add to basketItem description from seller
Print on Demand. This book analyzes when aggregating data is advantageous in regression analysis, which is when you combine multiple datasets to create one larger dataset. The author posits that this can sometimes be less effective than using smaller, disaggregated datasets, a concept called "aggregation bias." To illustrate, the author uses a linear regression model to explore a hypothetical relationship between micro and macro (aggregate) levels. They use the limiting properties of macrovariance to show that unless the elements of the explanatory variables are all bounded it is advantageous to use disaggregated data. The author concludes by showing relationships between Theil aggregation weights and micro-level proportions. This book will be essential reading for econometricians and statisticians, especially those working in fields that often require aggregating data. 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.…
Seller Inventory # 9781528557535_0
- Title
- The Variances of Regression Coefficient Estimates Using Aggregate Data
- Author
- Roy E. Welsch, Edwin Kuh
- Publisher
- Forgotten Books
- Publication year
- 2018
- Condition
- New
- Book Type
- print-on-demand item
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1528557530
- ISBN 13
- 9781528557535
- Seller catalogs
- Statistics
The Variances of Regression Coefficient Estimates Using Aggregate Data explains how pooling many individual data series into one big set can change the precision of regression results.
It shows when aggregation reduces macro variance relative to micro variance and when casual data grouping might cause trouble.
Written in clear terms, the work discusses models where micro parameters evolve over time and how these dynamics affect estimation. It also connects abstract theory to practical research through topics like aggregation weights and the relation between micro and macro parameters. The discussion offers guidance on choosing aggregation approaches that keep key assumptions plausible while improving estimation reliability.
- Understand aggregation gain: when and why combining data can reduce the estimated macro variance.
- Learn about the conditions that keep estimation stable as the data pool grows, including variance bounds and the role of correlation among units.
- Explore Theil-style aggregation weights and how micro parameters relate to macro estimates in practice.
- Get practical insights for empirical research using aggregated data without overpromising outcomes.
Ideal for readers of econometrics and research methodology who want a rigorous, accessible look at how aggregate data shapes regression inference.
"Synopsis" may belong to another edition of this title.
Forgotten Books
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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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