Parameter Estimation, Model Selection And Multi-Step Forecasting For A Long Memory Time Series: to 25; Pages:26 to 50; Pages:51 to 75; Pages:76 to 100; Pages:101 to 120 PDF Download
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Author: Julia Brodsky
Publisher:
ISBN: 9780591599701
Category :
Languages : en
Pages : 120
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Book Description
Author: Julia Brodsky
Publisher:
ISBN: 9780591599701
Category :
Languages : en
Pages : 120
Get Book Here
Book Description
Author: Julia Brodsky
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Wilfredo Palma
Publisher: John Wiley & Sons
ISBN: 0470131454
Category : Mathematics
Languages : en
Pages : 306
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Book Description
A self-contained, contemporary treatment of the analysis of long-range dependent data Long-Memory Time Series: Theory and Methods provides an overview of the theory and methods developed to deal with long-range dependent data and describes the applications of these methodologies to real-life time series. Systematically organized, it begins with the foundational essentials, proceeds to the analysis of methodological aspects (Estimation Methods, Asymptotic Theory, Heteroskedastic Models, Transformations, Bayesian Methods, and Prediction), and then extends these techniques to more complex data structures. To facilitate understanding, the book: Assumes a basic knowledge of calculus and linear algebra and explains the more advanced statistical and mathematical concepts Features numerous examples that accelerate understanding and illustrate various consequences of the theoretical results Proves all theoretical results (theorems, lemmas, corollaries, etc.) or refers readers to resources with further demonstration Includes detailed analyses of computational aspects related to the implementation of the methodologies described, including algorithm efficiency, arithmetic complexity, CPU times, and more Includes proposed problems at the end of each chapter to help readers solidify their understanding and practice their skills A valuable real-world reference for researchers and practitioners in time series analysis, economerics, finance, and related fields, this book is also excellent for a beginning graduate-level course in long-memory processes or as a supplemental textbook for those studying advanced statistics, mathematics, economics, finance, engineering, or physics. A companion Web site is available for readers to access the S-Plus and R data sets used within the text.
Author: Michael P. Clements
Publisher: OUP USA
ISBN: 0195398645
Category : Business & Economics
Languages : en
Pages : 732
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Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Author: Uwe Hassler
Publisher: John Wiley & Sons
ISBN: 1119470285
Category : Mathematics
Languages : en
Pages : 292
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Book Description
Provides a simple exposition of the basic time series material, and insights into underlying technical aspects and methods of proof Long memory time series are characterized by a strong dependence between distant events. This book introduces readers to the theory and foundations of univariate time series analysis with a focus on long memory and fractional integration, which are embedded into the general framework. It presents the general theory of time series, including some issues that are not treated in other books on time series, such as ergodicity, persistence versus memory, asymptotic properties of the periodogram, and Whittle estimation. Further chapters address the general functional central limit theory, parametric and semiparametric estimation of the long memory parameter, and locally optimal tests. Intuitive and easy to read, Time Series Analysis with Long Memory in View offers chapters that cover: Stationary Processes; Moving Averages and Linear Processes; Frequency Domain Analysis; Differencing and Integration; Fractionally Integrated Processes; Sample Means; Parametric Estimators; Semiparametric Estimators; and Testing. It also discusses further topics. This book: Offers beginning-of-chapter examples as well as end-of-chapter technical arguments and proofs Contains many new results on long memory processes which have not appeared in previous and existing textbooks Takes a basic mathematics (Calculus) approach to the topic of time series analysis with long memory Contains 25 illustrative figures as well as lists of notations and acronyms Time Series Analysis with Long Memory in View is an ideal text for first year PhD students, researchers, and practitioners in statistics, econometrics, and any application area that uses time series over a long period. It would also benefit researchers, undergraduates, and practitioners in those areas who require a rigorous introduction to time series analysis.
Author: JAN G. DE GOOIJER
Publisher:
ISBN:
Category :
Languages : en
Pages : 756
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Book Description
Author: Chris Chatfield
Publisher: CRC Press
ISBN: 1420036203
Category : Business & Economics
Languages : en
Pages : 281
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Book Description
From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space
Author: John Haywood
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We propose a general test of whether a time-series model, with parameters estimated by minimizing the single-step forecast error sum of squares, is robust with respect to multi-step prediction, for some specified lead time. The test may be applied to a, possibly seasonal, autoregressive integrated moving average (ARIMA) model using the parameters and residuals following maximum likelihood estimation. It is based on a score statistic, evaluated at these estimated parameters, which measures the sensitivity of the multi-step forecast error variance with respect to the parameters. We derive the large sample properties of the test and show by a simulation study that it has acceptable small sample size properties for higher lead times when applied to the integrated moving average or IMA model that gives rise to the exponentially weighted moving average predictor. We investigate the power of the test when the IMA(1,1) model has been fitted to an ARMA(1,1) process. Further, we demonstrate the high power of the test when an AR is fitted to a process generated as the sum of a stochastic trend and cycle plus noise. We use frequency domain methods for the derivation and sampling properties of the test, and to give insight into its application. The test is illustrated on two real series.
Author: George E. P. Box
Publisher: John Wiley & Sons
ISBN: 1118674928
Category : Mathematics
Languages : en
Pages : 709
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Book Description
Praise for the Fourth Edition "The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control." —Mathematical Reviews Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject. Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include: A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time series An expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory models Numerous examples drawn from finance, economics, engineering, and other related fields The use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecasting Updates to literature references throughout and new end-of-chapter exercises Streamlined chapter introductions and revisions that update and enhance the exposition Time Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.
Author: International Statistical Institute
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 556
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Book Description
V. 1-5, v. 7-10 include "Bulletin bibliographique."
