Multiple Time Series Models

Multiple Time Series Models

Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression.

Author: Patrick T. Brandt

Publisher: SAGE

ISBN: 9781412906562

Category: Mathematics

Page: 99

View: 753

Multiple Time Series Models introduces researchers and students to the different approaches to modeling multivariate time series data including simultaneous equations, ARIMA, error correction models, and vector autoregression. Authors Patrick T. Brandt and John T. Williams focus on vector autoregression (VAR) models as a generalization of these other approaches and discuss specification, estimation, and inference using these models.
Categories: Mathematics

Multiple Time Series Modeling Using the SAS VARMAX Procedure

Multiple Time Series Modeling Using the SAS VARMAX Procedure

To help in such cases, various test procedures have been suggested and
implemented in SAS. The hypothesis of stationarity is not, however, a simple
hypothesis: Many different time series models are formulated for stationary time
series.

Author: Anders Milhoj

Publisher: SAS Institute

ISBN: 9781629597492

Category: Computers

Page: 210

View: 806

Aimed at econometricians who have completed at least one course in time series modeling, this comprehensive book will teach you the time series analytical possibilities that SAS offers today. --
Categories: Computers

Forecasting Structural Time Series Models and the Kalman Filter

Forecasting  Structural Time Series Models and the Kalman Filter

A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.

Author: Andrew C. Harvey

Publisher: Cambridge University Press

ISBN: 0521405734

Category: Business & Economics

Page: 554

View: 818

A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.
Categories: Business & Economics

New Introduction to Multiple Time Series Analysis

New Introduction to Multiple Time Series Analysis

The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic.

Author: Helmut Lütkepohl

Publisher: Springer Science & Business Media

ISBN: 3540262393

Category: Business & Economics

Page: 764

View: 787

This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.
Categories: Business & Economics

Elements of Multivariate Time Series Analysis

Elements of Multivariate Time Series Analysis

Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data.

Author: Gregory C. Reinsel

Publisher: Springer Science & Business Media

ISBN: 0387406190

Category: Mathematics

Page: 358

View: 982

Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures.
Categories: Mathematics

Modeling Univariate and Multiple Time Series

Modeling Univariate and Multiple Time Series

Model checking in time series analysis . Proc . ASA - CENSUS - NBER
Conference on Applied Time Series Analysis of Economic Data . ( to appear ) .
Osborn , D. R. ( 1977 ) . Exact and approximate maximum likelihood estimators
for vector ...

Author: Ruey Shiong Tsay

Publisher:

ISBN: WISC:89010951069

Category: Time-series analysis

Page: 262

View: 684

Categories: Time-series analysis

Multiple Time Series

Multiple Time Series

This series provides essential and invaluable reading for all statisticians, whether in academia, industry, government, or research.

Author: Edward James Hannan

Publisher: John Wiley & Sons Incorporated

ISBN: UCSD:31822003704335

Category: Mathematics

Page: 536

View: 485

The Wiley Series in Probability and Statistics is a collection of topics of current research interests in both pure and applied statistics and probability developments in the field and classical methods. This series provides essential and invaluable reading for all statisticians, whether in academia, industry, government, or research.
Categories: Mathematics

Time Series Analysis and Forecasting by Example

Time Series Analysis and Forecasting by Example

The book presents methodologies for time series analysis in a simplified, example-based approach.

Author: Søren Bisgaard

Publisher: John Wiley & Sons

ISBN: 1118056957

Category: Mathematics

Page: 400

View: 341

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.
Categories: Mathematics

Time Series Techniques for Economists

Time Series Techniques for Economists

This book brings together recent research in the application of time series techniques and analyses the areas of most importance to applied economics.

Author: Terence C Mills

Publisher: Cambridge University Press

ISBN: 0521405742

Category: Business & Economics

Page: 377

View: 466

This book brings together recent research in the application of time series techniques and analyses the areas of most importance to applied economics.
Categories: Business & Economics

Time Series Analysis and Macroeconometric Modelling

Time Series Analysis and Macroeconometric Modelling

' - Aslib Book Guide This major volume of essays by Kenneth F. Wallis features 28 articles published over a quarter of a century on the statistical analysis of economic time series, large-scale macroeconometric modelling, and the interface ...

Author: Kenneth Frank Wallis

Publisher: Edward Elgar Publishing

ISBN: 1782541624

Category: Business & Economics

Page: 426

View: 901

'An excellent reference volume of this author's work, bringing together articles published over a 25 year span on the statistical analysis of economic time series, large scale macroeconomic modelling and the interface between them.' - Aslib Book Guide This major volume of essays by Kenneth F. Wallis features 28 articles published over a quarter of a century on the statistical analysis of economic time series, large-scale macroeconometric modelling, and the interface between them. The first part deals with time-series econometrics and includes significant early contributions to the development of the LSE tradition in time-series econometrics, which is the dominant British tradition and has considerable influence worldwide. Later sections discuss theoretical and practical issues in modelling seasonality and forecasting with applications in both large-scale and small-scale models. The final section summarizes the research programme of the ESRC Macroeconomic Modelling Bureau, a unique comparison project among economy-wide macroeconometric models.
Categories: Business & Economics

Forecasting Economic Time Series

Forecasting Economic Time Series

This book has been updated to reflect developments in time series analysis and forecasting theory and practice, particularly as applied to economics.

Author: Clive William John Granger

Publisher:

ISBN: STANFORD:36105002640683

Category: Economic forecasting

Page: 333

View: 130

This book has been updated to reflect developments in time series analysis and forecasting theory and practice, particularly as applied to economics. The second edition pays attention to such problems as how to evaluate and compare forecasts.
Categories: Economic forecasting

Analysis of Economic Time Series

Analysis of Economic Time Series

In this edition which has been reprinted with corrections, Nerlove and his co-authors illustrate techniques of spectral analysis and methods based on parametric models in the analysis of economic time series.

Author: Marc Nerlove

Publisher: Emerald Group Pub Limited

ISBN: 0125157517

Category: Business & Economics

Page: 468

View: 551

In this edition which has been reprinted with corrections, Nerlove and his co-authors illustrate techniques of spectral analysis and methods based on parametric models in the analysis of economic time series. The book provides a means and a method for incorporating economic intuition and theory in the formulation of time-series models useful in forecasting, in the formulation and estimation of distributed lag models, and in other applications, such as seasonal adjustment. Analysis of Economic Time Series is a useful primary text for graduate students and an attractive reference for researchers. Key Features * Presents a self-contained treatment of Fourier Analysis and complex variables, as well as Spectral Analysis of time series * Includes a detailed treatment of unobserved-components (UC) models and their time-series properties by means of covariance-generating transforms * Provides the formulation and maximum-likelihood estimation of ARMA and UC models in both time and frequency domains Integrates several topics in time-series analysis: * The formulation and estimation of distributed-lag models of dynamic economic behavior * The application of the techniques of spectral analysis in the study of behavior of economic time series * Unobserved-components models for economic time series and the closely related problem of seasonal adjustment * The complimentarities between time-domain and frequency-domain approaches to the analysis of economic time series * Historical contributions extending from the time of Charles Babbage and the Edinburgh Review to the present * Treats spectral analysis and Box-Jenkins models for an intuitive but rigorous point of view * Shows how these two types of analysis may be synthesized so that they complement one another * Describes a new type of model, based on a superposition of Box-Jenkins models, that captures the essential idea of the unobserved-components models long used in the analysis of economic time series * Applies multiple time-series techniques to the estimation of a novel dynamic model of the US cattle industry
Categories: Business & Economics

Advanced Time Series Data Analysis

Advanced Time Series Data Analysis

Introduces the latest developments in forecasting in advanced quantitative data analysis This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample ...

Author: I. Gusti Ngurah Agung

Publisher: Wiley

ISBN: 9781119504719

Category: Mathematics

Page: 544

View: 983

Introduces the latest developments in forecasting in advanced quantitative data analysis This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable. Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers. Presents models that are all classroom tested Contains real-life data samples Contains over 350 equation specifications of various time series models Contains over 200 illustrative examples with special notes and comments Applicable for time series data of all quantitative studies Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
Categories: Mathematics

Time Series Analysis

Time Series Analysis

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject.

Author: George E. P. Box

Publisher: Wiley

ISBN: 0470272848

Category: Mathematics

Page: 784

View: 329

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series as well as 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, modern topics are introduced through the book's new features, which include: A new chapter on multivariate time series analysis, including a discussion of the challenge that arise with their modeling and an outline of the necessary analytical tools New coverage of forecasting in the design of feedback and feedforward control schemes A new chapter on nonlinear and long memory models, which explores additional models for application such as heteroscedastic time series, nonlinear time series models, and models for long memory processes Coverage of structural component models for the modeling, forecasting, and seasonal adjustment of time series A review of the maximum likelihood estimation for ARMA models with missing values Numerous illustrations and detailed appendices supplement the book,while extensive references and discussion questions at the end of each chapter facilitate an in-depth understanding of both time-tested and modern concepts. With its focus on practical, rather than heavily mathematical, techniques, Time Series Analysis, Fourth Edition is the upper-undergraduate and graduate levels. this book is also an invaluable reference for applied statisticians, engineers, and financial analysts.
Categories: Mathematics

JMR Journal of Marketing Research

JMR  Journal of Marketing Research

The multiple ARMA ( 2 , 9 ) model is a multivariate generalization of the
univariate time series model . It can be written as ( 1 ) ( B ) z , = O ( B ) a , users
had to develop their own computer software . The programs used by Moriarty and
Salamon ...

Author:

Publisher:

ISBN: UCSD:31822008381469

Category: Marketing research

Page:

View: 545

Categories: Marketing research

Non linear Time Series

Non linear Time Series

7.4 Incorporating covariates: the first few words on non-linear multiple time series
modelling Up to now we have been dealing with the modelling of a single time
series only. Incorporating covariates into our modelling requires multiple time ...

Author: Howell Tong

Publisher: Oxford University Press, USA

ISBN: MINN:31951D00520332J

Category: Mathematics

Page: 564

View: 195

Written by an internationally recognized expert in the field, this book provides a valuable introduction to the rapidly growing area of non-linear time series. Because developments in the study of dynamical systems have motivated many of the advances discussed here, the author's coverage includes such fundamental concepts of dynamical systems theory as limit cycles, Lyapunov functions, thresholds, and stability, with detailed descriptions of their role in the analysis of non-linear time series data. As the first accessible and comprehensive account of these exciting new developments, this unique volume bridges the gap between linear and chaotic time series analysis. Both statisticians and dynamical systems theorists will value its survey of recent developments and the present state of research, as well as the discussion of a number of unsolved problems in the field.
Categories: Mathematics

Essays on Common Dynamic Features in Multiple Time Series

Essays on Common Dynamic Features in Multiple Time Series

Hayashi , F. and Sims , C. ( 1983 ) , " Nearly Efficient Estimation of Time Series
Models with Predetermined , But Not Exogenous , Instruments , " Econometrica ,
51 , pp . 783-798 . Hendry , D. F. ( 1989 ) , " Lectures on Econometric
Methodology ...

Author: Farshid Vahid-Araghi

Publisher:

ISBN: UCSD:31822007607013

Category: Time-series analysis

Page: 220

View: 457

Categories: Time-series analysis