Applied Time Series Analysis

Author: Wayne A. Woodward
Publisher: CRC Press
ISBN: 1439818371
Size: 38.55 MB
Format: PDF
View: 829
Virtually any random process developing chronologically can be viewed as a time series. In economics, closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis includes examples across a variety of fields, develops theory, and provides software to address time series problems in a broad spectrum of fields. The authors organize the information in such a format that graduate students in applied science, statistics, and economics can satisfactorily navigate their way through the book while maintaining mathematical rigor. One of the unique features of Applied Time Series Analysis is the associated software, GW-WINKS, designed to help students easily generate realizations from models and explore the associated model and data characteristics. The text explores many important new methodologies that have developed in time series, such as ARCH and GARCH processes, time varying frequencies (TVF), wavelets, and more. Other programs (some written in R and some requiring S-plus) are available on an associated website for performing computations related to the material in the final four chapters.

Introduction To Time Series Modeling

Author: Genshiro Kitagawa
Publisher: CRC Press
ISBN: 9781584889229
Size: 72.13 MB
Format: PDF, ePub
View: 2421
In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, Introduction to Time Series Modeling covers numerous time series models and the various tools for handling them. The book employs the state-space model as a generic tool for time series modeling and presents convenient recursive filtering and smoothing methods, including the Kalman filter, the non-Gaussian filter, and the sequential Monte Carlo filter, for the state-space models. Taking a unified approach to model evaluation based on the entropy maximization principle advocated by Dr. Akaike, the author derives various methods of parameter estimation, such as the least squares method, the maximum likelihood method, recursive estimation for state-space models, and model selection by the Akaike information criterion (AIC). Along with simulation methods, he also covers standard stationary time series models, such as AR and ARMA models, as well as nonstationary time series models, including the locally stationary AR model, the trend model, the seasonal adjustment model, and the time-varying coefficient AR model. With a focus on the description, modeling, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It encourages readers to build models for their own real-life problems.

Models For Dependent Time Series

Author: Granville Tunnicliffe Wilson
Publisher: CRC Press
ISBN: 1420011502
Size: 20.81 MB
Format: PDF, ePub
View: 1569
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data. The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational material for the remaining chapters, which cover the construction of structural models and the extension of vector autoregressive modeling to high frequency, continuously recorded, and irregularly sampled series. The final chapter combines these approaches with spectral methods for identifying causal dependence between time series. Web Resource A supplementary website provides the data sets used in the examples as well as documented MATLAB® functions and other code for analyzing the examples and producing the illustrations. The site also offers technical details on the estimation theory and methods and the implementation of the models.

Handbook Of Applied Economic Statistics

Author: Aman Ullah
Publisher: CRC Press
ISBN: 9780824701291
Size: 65.87 MB
Format: PDF, ePub, Docs
View: 978
This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

Applied Statistical Inference With Minitab

Author: Sally Lesik
Publisher: CRC Press
ISBN: 142006584X
Size: 80.23 MB
Format: PDF, Mobi
View: 7195
Through clear, step-by-step mathematical calculations, Applied Statistical Inference with MINITAB enables students to gain a solid understanding of how to apply statistical techniques using a statistical software program. It focuses on the concepts of confidence intervals, hypothesis testing, validating model assumptions, and power analysis. Illustrates the techniques and methods using MINITAB After introducing some common terminology, the author explains how to create simple graphs using MINITAB and how to calculate descriptive statistics using both traditional hand computations and MINITAB. She then delves into statistical inference topics, such as confidence intervals and hypothesis testing, as well as linear regression, including the Ryan–Joiner test. Moving on to multiple regression analysis, the text addresses ANOVA, the issue of multicollinearity, assessing outliers, and more. It also provides a conceptual introduction to basic experimental design and one-way ANOVA. The final chapter discusses two-way ANOVA, nonparametric analyses, and time series analysis. Establishes a foundation for studying more complex topics Ideal for students in the social sciences, this text shows how to implement basic inferential techniques in practice using MINITAB. It establishes the foundation for students to build on work in more advanced inferential statistics.

Applied Time Series Analysis

Author: David F. Findley
Publisher: Academic Press
ISBN: 1483263940
Size: 15.57 MB
Format: PDF, Docs
View: 1538
Applied Time Series Analysis contains the proceedings of the First Applied Time Series Symposium held in Tulsa, Oklahoma, on May 14-15, 1976. The symposium provided a forum for reviewing various applications of time series analysis and covered topics ranging from nonlinear time series modeling and G-spectral estimation to multivariate autoregression estimation using residuals. Adaptive processing of seismic data and the application of homomorphic filtering to seismic data processing are also discussed. Comprised of 10 chapters, this book begins by describing the application of parametric models to the analysis and control of time series using some numerical examples. The reader is then introduced to nonlinear time series modeling; two-dimensional recursive filtering in theory and practice; and spectral estimators. Waves propagating in random media as statistical time series are also considered. The book concludes with a chapter that illustrates how the intensity of a Poisson process is estimated, with emphasis on a time series approach to the fixed signal case, invariant testing, and spline estimation. This monograph will be a useful resource for students and practitioners in the fields of mathematics and statistics, electrical engineering, and computer science.

Diagnostic Checks In Time Series

Author: Wai Keung Li
Publisher: CRC Press
ISBN: 9780203485606
Size: 25.10 MB
Format: PDF, Kindle
View: 6655
Diagnostic checking is an important step in the modeling process. But while the literature on diagnostic checks is quite extensive and many texts on time series modeling are available, it still remains difficult to find a book that adequately covers methods for performing diagnostic checks. Diagnostic Checks in Time Series helps to fill that gap. Author Wai Keung Li--one of the world's top authorities in time series modeling--concentrates on diagnostic checks for stationary time series and covers a range of different linear and nonlinear models, from various ARMA, threshold type, and bilinear models to conditional non-Gaussian and autoregressive heteroscedasticity (ARCH) models. Because of its broad applicability, the portmanteau goodness-of-fit test receives particular attention, as does the score test. Unlike most treatments, the author's approach is a practical one, and he looks at each topic through the eyes of a model builder rather than a mathematical statistician. This book brings together the widely scattered literature on the subject, and with clear explanations and focus on applications, it guides readers through the final stages of their modeling efforts. With Diagnostic Checks in Time Series, you will understand the relative merits of the models discussed, know how to estimate these models, and often find ways to improve a model.

State Space Methods For Time Series Analysis

Author: Jose Casals
Publisher: CRC Press
ISBN: 1482219603
Size: 66.42 MB
Format: PDF, Docs
View: 6066
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.

The Analysis Of Time Series

Author: Chris Chatfield
Publisher: CRC Press
ISBN: 9780203491683
Size: 45.50 MB
Format: PDF, Kindle
View: 6713
Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets. The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from A free online appendix on time series analysis using R can be accessed at Highlights of the Sixth Edition: A new section on handling real data New discussion on prediction intervals A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series A new chapter of examples and practical advice Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.

Robust Regression

Author: Lawrence
Publisher: CRC Press
ISBN: 9780824781293
Size: 24.64 MB
Format: PDF, ePub, Mobi
View: 1254
Combining theory, methodology, and applications in a unified survey, this important reference/text presents the most recent results in robust regression analysis, including properties of robust regression techniques, computational issues, forecasting, and robust ridge regression. It provides useful case studies so that students and engineers can apply these techniques to forecasting, quantitative business analysis, econometrics, marketing, statistics, and demand modeling. Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weight each observation ... discusses generalized properties of L[subscript p]-estimators ... includes an algorithm for identifying outliers using least absolute value criterion in regression modeling ... reviews redescending M-estimators ... studies L[subscript 1] linear regression ... proposes the best linear unbiased estimators for fixed parameters and random errors in the mixed linear model ... summarizes known properties of L[subscript 1] estimators for time series analysis ... examines ordinary least squares, latent root regression, and a robust regression weighting scheme ... and evaluates results from five different robust ridge regression estimators. Containing 120 tables and diagrams plus numerous bibliographic citations, Robust Regression: Analysis and Applications is the leading reference for applied statisticians, operations researchers, econometricians, marketing forecasters, business administration and management scientists, and industrial engineers as well as a text for graduate statistics or economics courses. Book jacket.