Time Series Analysis for the Social Sciences, Box-Steffensmeier

Автор: Hamilton, James
Название: Time Series Analysis
ISBN: 0691042896 ISBN-13(EAN): 9780691042893
Издательство: Wiley
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Цена: 73920.00 T
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Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.

Автор: Durbin, James; Koopman, Siem Jan
Название: Time Series Analysis by State Space Methods
ISBN: 019964117X ISBN-13(EAN): 9780199641178
Издательство: Oxford Academ
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Цена: 121440.00 T
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Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.

Автор: Imbens
Название: Causal Inference for Statistics, Social, and Biomedical Sciences
ISBN: 0521885884 ISBN-13(EAN): 9780521885881
Издательство: Cambridge Academ
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Цена: 54910.00 T
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Описание: This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

Автор: Ruey Tsay
Название: Analysis of Financial Time Series
ISBN: 0470414359 ISBN-13(EAN): 9780470414354
Издательство: Wiley
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Цена: 131950.00 T
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Описание: Analysis of Financial Time Series, Third Edition provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.

Автор: T. W. Anderson
Название: An Introduction to Multivariate Statistical Analysis, Third Edition
ISBN: 0471360910 ISBN-13(EAN): 9780471360919
Издательство: Wiley
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Цена: 184750.00 T
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Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.

Автор: Bevington; Robinson
Название: Data Reduction And Error Analysis For The Physical Sciences
ISBN: 0071199268 ISBN-13(EAN): 9780071199261
Издательство: McGraw-Hill
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Цена: 49180.00 T
Наличие на складе: Поставка под заказ.
Описание: Provides an introduction to the concepts of statistical analysis of data for students at undergraduate and graduate level. This text also provides tools for data reduction and error analysis commonly required in the physical sciences. It features a variety of numerical and graphical techniques, and emphasizes methods of handling data than theory.

Автор: Ruey S. Tsay
Название: Multivariate Time Series Analysis: With R and Financial Applications
ISBN: 1118617908 ISBN-13(EAN): 9781118617908
Издательство: Wiley
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Цена: 125610.00 T
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Описание: An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series.

Автор: Pena, Daniel S. Tiao, George C. Tsay, Ruey S.
Название: Course in time series analysis
ISBN: 047136164X ISBN-13(EAN): 9780471361640
Издательство: Wiley
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Цена: 209030.00 T
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Описание: New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data.

Автор: Douglas C. Montgomery,Cheryl L. Jennings,Murat Kul
Название: Introduction to Time Series Analysis and Forecasting
ISBN: 1118745116 ISBN-13(EAN): 9781118745113
Издательство: Wiley
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Цена: 121390.00 T
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Описание: Praise for the First Edition " [t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics.

Автор: Commandeur, Jacques J.F.; Koopman, Siem Jan
Название: An Introduction to State Space Time Series Analysis
ISBN: 0199228876 ISBN-13(EAN): 9780199228874
Издательство: Oxford Academ
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Цена: 51210.00 T
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Описание: This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics.

Автор: Box G. E. P.
Название: Time Series Analysis: Forecasting and Control, 4th Edition
ISBN: 0470272848 ISBN-13(EAN): 9780470272848
Издательство: Wiley
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Цена: 123550.00 T
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Описание: This is a revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, modeling the effects of intervention events, and process control, among others. In addition to meticulous modifications in content and improvements in style, the new edition incorporates several new topics in an effort to modernize the subject matter. These topics include extensive discussions of multivariate time series, smoothing, likelihood function based on the state space model, autoregressive models, structural component models and deterministic seasonal components, and nonlinear and long memory models.

Автор: Holger Kantz
Название: Nonlinear Time Series Analysis
ISBN: 0521529026 ISBN-13(EAN): 9780521529020
Издательство: Cambridge Academ
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Цена: 82370.00 T
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Описание: The time variability of many natural and social phenomena is not well described by standard methods of data analysis. Nonlinear time series analysis uses chaos theory and nonlinear dynamics to understand such seemingly unpredictable behaviour. Results are applied to real data from physics, biology, medicine and engineering.