Econometrics of Short and Unreliable Time Series, Thomas Url; Andreas W?rg?tter

Автор: Shafique, Muhammad Henkel, Jorg Rehman, Semeen
Название: Reliable software for unreliable hardware
ISBN: 3319257706 ISBN-13(EAN): 9783319257709
Издательство: Springer
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Цена: 15672.00 р.
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Описание: Readers will learn how to achieve increased soft error resilience on unreliable hardware, while exploiting the inherent error masking characteristics and error (stemming from soft errors, aging, and process variations) mitigations potential at different software layers.

Автор: Jean-Marie Dufour; Baldev Raj
Название: New Developments in Time Series Econometrics
ISBN: 3642487440 ISBN-13(EAN): 9783642487446
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Since these themes are closely inter-related, several other topics covered are also worth stressing: vector autoregressive (VAR) models, cointegration and error-correction models, nonparametric methods in time series, and fractionally integrated models.

Автор: Mills
Название: Time Series Econometrics
ISBN: 1137525320 ISBN-13(EAN): 9781137525321
Издательство: Springer
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Цена: 12157.00 р.
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Описание: This book provides an introductory treatment of time series econometrics, a subject that is of key importance to both students and practitioners of economics. It contains material that any serious student of economics and finance should be acquainted with if they are seeking to gain an understanding of a real functioning economy.

Автор: Neusser
Название: Time Series Econometrics
ISBN: 3319328611 ISBN-13(EAN): 9783319328614
Издательство: Springer
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Цена: 13974.00 р.
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Описание: This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.