The Econometrics of Panel Data, L?szlo M?ty?s; Patrick Sevestre

Автор: Brooks
Название: Introductory Econometrics for Finance
ISBN: 1107661455 ISBN-13(EAN): 9781107661455
Издательство: Cambridge Academ
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Цена: 7918.00 р.
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Описание: This bestselling and thoroughly classroom-tested textbook is a complete resource for finance students. A comprehensive and illustrated discussion of the most common empirical approaches in finance prepares students for using econometrics in practice, while detailed case studies help them understand how the techniques are used in relevant financial contexts. Worked examples from the latest version of the popular statistical software EViews guide students to implement their own models and interpret results. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Building on the successful data- and problem-driven approach of previous editions, this third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time. A companion website, with numerous student and instructor resources, completes the learning package.

Автор: Erik Biorn
Название: Econometrics of Panel Data
ISBN: 0198753446 ISBN-13(EAN): 9780198753445
Издательство: Oxford Academ
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Цена: 14256.00 р.
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Описание: A graduate text on panel data that takes the reader gradually from simple models and methods in scalar (simple vector) notation to more complex models in matrix notation.

Автор: Laszlo Matyas
Название: The Econometrics of Panel Data: Handbook of Theory and Applications
ISBN: 9401066558 ISBN-13(EAN): 9789401066556
Издательство: Springer
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Цена: 12157.00 р.
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Описание: The aim of this volume is to provide a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. Part II deals with nonlinear models and related issues: logit and probit models, latent variable models, incomplete panels and selectivity bias, and point processes.

Автор: L?szl? M?ty?s; Patrick Sevestre
Название: The Econometrics of Panel Data
ISBN: 9401065489 ISBN-13(EAN): 9789401065481
Издательство: Springer
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Цена: 12157.00 р.
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Описание: Part II deals with nonlinear models and related issues: logit and pro bit models, latent variable models, duration and count data models, incomplete panels and selectivity bias, point processes, and simulation techniques.

Автор: Marc Nerlove
Название: Essays in Panel Data Econometrics
ISBN: 0521815347 ISBN-13(EAN): 9780521815345
Издательство: Cambridge Academ
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Цена: 12830.00 р.
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Описание: This volume collects seven of Marc Nerlove`s previously published, classic essays on panel data econometrics, and a cogent essay on the history of the subject. The essays illustrate both the role of the substantive context in shaping appropriate methods of inference and the increasing importance of computer-intensive methods.

Автор: Agung I Gusti Ngurah
Название: Panel Data Analysis Using eViews
ISBN: 1118715586 ISBN-13(EAN): 9781118715581
Издательство: Wiley
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Цена: 16466.00 р.
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Описание: A comprehensive and accessible guide to panel data analysis using EViews software This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets.

Автор: Hsiao, Cheng,
Название: Analysis of Panel Data
ISBN: 1107657636 ISBN-13(EAN): 9781107657632
Издательство: Cambridge Academ
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Цена: 5859.00 р.
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Описание: This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.