Analysis of Panel Data
Series: Econometric Society Monographs;
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19 732 Ft
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Product details:
- Edition number 4, New edition
- Publisher Cambridge University Press
- Date of Publication 7 July 2022
- ISBN 9781009060752
- Binding Paperback
- No. of pages500 pages
- Size 243x168x28 mm
- Weight 910 g
- Language English 578
Categories
Short description:
A comprehensive introduction of fundamental panel data methodologies.
MoreLong description:
Now in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition's primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge's Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.
A masterful new edition of Hsiao's classic text on panel data. This is a superbly comprehensive and accessible source for panel data with modern approaches to inference and identification, helpful to econometricians and other quantitative social scientists. Esfandiar Maasoumi, Emery University
Table of Contents:
Preface; 1. Introduction; 2. Static models with additive effects; 3. Dynamic models with additive effects; 4. Static simultaneous models with additive effects; 5. Dynamic system; 6. Qualitative choice models; 7. Limited dependent and sample section models; 8. Some nonlinear models; 9. Miscellaneous topics; 10. Interactive effects models; 11. Spatial models and cross-sectional dependent data; 12. Program evaluation; 13. Varying coefficients models; 14. Big data analysis.
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