Modelling Nonlinear Economic Time Series, Terasvirta Clive W J

Автор: Martin
Название: Econometric Modelling with Time Series
ISBN: 0521139813 ISBN-13(EAN): 9780521139816
Издательство: Cambridge Academ
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Цена: 11722.00 р.
Наличие на складе: Ожидается поступление.

Описание: This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.

Автор: Terence C. Mills
Название: The Econometric Modelling of Financial Time Series
ISBN: 052171009X ISBN-13(EAN): 9780521710091
Издательство: Cambridge Academ
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Цена: 7445.00 р.
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Описание: This best-selling graduate textbook provides detailed coverage of the latest research techniques and findings relating to the empirical analysis of financial markets. This third edition contains a wealth of material reflecting the developments of the last decade, including a new chapter on nonlinearity and its testing.

Автор: Fan Jianqing, Yao Qiwei
Название: Nonlinear Time Series / Nonparametric and Parametric Methods
ISBN: 0387261427 ISBN-13(EAN): 9780387261423
Издательство: Springer
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Цена: 15372.00 р.
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Описание: This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modelling in state-space, in frequency-domain as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced. The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have the value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology and ecology. The prerequisites are basic courses in probability and statistics. Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial econometrics, analysis of longitudinal data, model selection, wavelets and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics.

Автор: Mills
Название: Modelling Trends and Cycles in Economic Time Series
ISBN: 1403902097 ISBN-13(EAN): 9781403902092
Издательство: Springer
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Цена: 9781.00 р.
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Описание: Modelling trends and cycles in economic time series has a long history, with the use of linear trends and moving averages forming the basic tool kit of economists until the 1970s.

Автор: Franses
Название: Time Series Models for Business and Economic Forecasting
ISBN: 0521520916 ISBN-13(EAN): 9780521520911
Издательство: Cambridge Academ
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Цена: 7445.00 р.
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Описание: With a new author team contributing decades of practical experience, this fully updated second edition textbook summarises the most critical decisions, techniques and steps in creating effective forecasting models. Includes all new theoretical and practical exercises geared at guiding students through the steps of creating forecasting models on their own.

Автор: Simon P. Burke; John Hunter; Alessandra Canepa
Название: Multivariate Modelling of Non-Stationary Economic Time Series
ISBN: 0230243304 ISBN-13(EAN): 9780230243309
Издательство: Springer
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Цена: 27950.00 р.
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Описание: This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models.

Автор: Simon P. Burke; John Hunter; Alessandra Canepa
Название: Multivariate Modelling of Non-Stationary Economic Time Series
ISBN: 0230243312 ISBN-13(EAN): 9780230243316
Издательство: Springer
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Цена: 8384.00 р.
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Описание: This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models.

Автор: Martin
Название: Econometric Modelling with Time Series
ISBN: 0521196604 ISBN-13(EAN): 9780521196604
Издательство: Cambridge Academ
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Цена: 16474.00 р.
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Описание: This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.