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Multivariate Time Series Analysis With Matlab Var And Varmax Models
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Book Synopsis MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS by : Perez M.
Download or read book MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS written by Perez M. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-24 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on Multivariate Time Series Models. The most important issues are the following: Vector Autoregressive Models Introduction to Vector Autoregressive (VAR) Models Data Structures Model Specification Structures VAR Model Estimation VAR Model Forecasting, Simulation, and Analysis VAR Model Case Study Cointegration and Error Correction Introduction to Cointegration Analysis Identifying Single Cointegrating Relations Identifying Multiple Cointegrating Relations Testing Cointegrating Vectors and Adjustment Speeds
Book Synopsis Multivariate Time Series Analysis With Matlab by : Mara Prez
Download or read book Multivariate Time Series Analysis With Matlab written by Mara Prez and published by CreateSpace. This book was released on 2014-09-12 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics Multivariate Time Series ModelsVector Autoregressive Models Introduction to Vector Autoregressive (VAR) Models Data Structures Model Specification Structures VAR and VARMAX Model Estimation VAR and VARMAX Model Forecasting, Simulation, and Analysis VAR and VARMAX Model Case Study Cointegration and Error Correction Introduction to Cointegration Analysis Identifying Single Cointegrating Relations Identifying Multiple Cointegrating Relations Testing Cointegrating Vectors and Adjustment Speeds
Book Synopsis Linear Time Series with MATLAB and OCTAVE by : Víctor Gómez
Download or read book Linear Time Series with MATLAB and OCTAVE written by Víctor Gómez and published by Springer Nature. This book was released on 2019-10-04 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an introduction to linear univariate and multivariate time series analysis, providing brief theoretical insights into each topic, and from the beginning illustrating the theory with software examples. As such, it quickly introduces readers to the peculiarities of each subject from both theoretical and the practical points of view. It also includes numerous examples and real-world applications that demonstrate how to handle different types of time series data. The associated software package, SSMMATLAB, is written in MATLAB and also runs on the free OCTAVE platform. The book focuses on linear time series models using a state space approach, with the Kalman filter and smoother as the main tools for model estimation, prediction and signal extraction. A chapter on state space models describes these tools and provides examples of their use with general state space models. Other topics discussed in the book include ARIMA; and transfer function and structural models; as well as signal extraction using the canonical decomposition in the univariate case, and VAR, VARMA, cointegrated VARMA, VARX, VARMAX, and multivariate structural models in the multivariate case. It also addresses spectral analysis, the use of fixed filters in a model-based approach, and automatic model identification procedures for ARIMA and transfer function models in the presence of outliers, interventions, complex seasonal patterns and other effects like Easter, trading day, etc. This book is intended for both students and researchers in various fields dealing with time series. The software provides numerous automatic procedures to handle common practical situations, but at the same time, readers with programming skills can write their own programs to deal with specific problems. Although the theoretical introduction to each topic is kept to a minimum, readers can consult the companion book ‘Multivariate Time Series With Linear State Space Structure’, by the same author, if they require more details.
Book Synopsis Econometric With Matlab by : A. Smith
Download or read book Econometric With Matlab written by A. Smith and published by . This book was released on 2017-11-10 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometrics Toolbox provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostics for model selection, including hypothesis tests, unit root,stationarity, and structural change.This book develops VAR, VARX, VARMA, VARMAX and VEC time series models.The most important content is the following:* Vector Autoregression (VAR) Models* Types of Multivariate Time Series Models* Lag Operator Representation* Stable and Invertible Models* Building VAR Models* Multivariate Time Series Data Structures* Multivariate Time Series Data* Data Preprocessing* Partitioning Response Data* Multivariate Time Series Model Creation* Models for Multiple Time Series* Creating VAR Models* Create and Adjust VAR Model Using Shorthand Syntax* Create and Adjust VAR Model Using Longhand Syntax* Model Objects with Known Parameters* Model Objects with No Parameter Values* Model Objects with Selected Parameter Values* VAR Model Estimation* Preparing VAR Models for Fitting* Fitting Models to Data* Examining the Stability of a Fitted Model* Convert VARMA Model to VAR Model* Fit VAR Model of CPI and Unemployment Rate* Fit VAR Model to Simulated Data* VAR Model Forecasting, Simulation, and Analysis* VAR Model Forecasting* Data Scaling* Calculating Impulse Responses* Generate Impulse Responses for a VAR model* Compare Generalized and Orthogonalized Impulse Response Functions* Forecast VAR Model* Forecast VAR Model Using Monte Carlo Simulation* Forecast VAR Model Conditional Responses* Multivariate Time Series Models with Regression Terms* Design Matrix Structure for Including Exogenous Data* Estimation of Models that Include Exogenous Data* Implement Seemingly Unrelated Regression Analyses* Implement Seemingly Unrelated Regression* Estimate Capital Asset Pricing Model Using SUR* Simulate Responses of Estimated VARX Model* Simulate VAR Model Conditional Responses* Simulate Responses Using filter* VAR Model Case Study* Cointegration and Error Correction Analysis* Determine Cointegration Rank of VEC Model* Identifying Single Cointegrating Relations* The Engle-Granger Test for Cointegration* Limitations of the Engle-Granger Test* Test for Cointegration Using the Engle-Granger Test* Estimate VEC Model Parameters Using egcitest* Simulate and Forecast a VEC Model* Generate VEC Model Impulse Responses* Identifying Multiple Cointegrating Relations* Test for Cointegration Using the Johansen Test* Estimate VEC Model Parameters Using jcitest* Compare Approaches to Cointegration Analysis* Testing Cointegrating Vectors and Adjustment Speeds* Test Cointegrating Vectors* Test Adjustment Speeds
Book Synopsis Multivariate Time Series Analysis by : Ruey S. Tsay
Download or read book Multivariate Time Series Analysis written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2013-11-11 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
Book Synopsis Multiple Time Series Modeling Using the SAS VARMAX Procedure by : Anders Milhoj
Download or read book Multiple Time Series Modeling Using the SAS VARMAX Procedure written by Anders Milhoj and published by SAS Institute. This book was released on 2016-01-11 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. --
Book Synopsis Time Series Analysis with Matlab. Arima and Arimax Models by : Perez M.
Download or read book Time Series Analysis with Matlab. Arima and Arimax Models written by Perez M. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-23 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometrics Toolbox(TM) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostic functions for model selection, including hypothesis, unit root, and stationarity tests.. This book especially developed ARIMA and ARIMAX models acfross BOX-JENKINS methodology
Book Synopsis Univariate Time Series Analysis with MATLAB by : Perez M.
Download or read book Univariate Time Series Analysis with MATLAB written by Perez M. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-26 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometrics Toolbox(tm) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostic functions for model selection, including hypothesis, unit root, and stationarity tests. This book focuses on Univariate Time Series Analysis.
Book Synopsis Time Series Analysis with MATLAB. Arima/Varmax/Garch/Gjr Models. Functions and Examples by : Karter J
Download or read book Time Series Analysis with MATLAB. Arima/Varmax/Garch/Gjr Models. Functions and Examples written by Karter J and published by Createspace Independent Publishing Platform. This book was released on 2016-10-15 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the MATLAB functions for working with time series and econometric models whose variables are time series. ARIMA Box Jenkins methodology, VARMAX multivariate models, models with conditional heteroskedasticity ARCH / GARCH / GJR and all kinds of econometric models with temporal dimension is included. All functions are treated with full syntax and illustrated with examples.
Book Synopsis Multivariate Time Series Analysis and Applications by : William W. S. Wei
Download or read book Multivariate Time Series Analysis and Applications written by William W. S. Wei and published by John Wiley & Sons. This book was released on 2019-03-18 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.
Author :Perez M. Publisher :Createspace Independent Publishing Platform ISBN 13 :9781534845459 Total Pages :204 pages Book Rating :4.8/5 (454 download)
Book Synopsis Time Series Analysis With Matlab by : Perez M.
Download or read book Time Series Analysis With Matlab written by Perez M. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-23 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometrics Toolbox(TM) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. This book focuses on conditional variance models. Conditional variance models attempt to address volatility clustering in univariate time series models to improve parameter estimates and forecast accuracy. To model volatility, Econometrics Toolbox(TM) supports the standard generalized autoregressive conditional heteroscedastic (ARCH/GARCH) model, the exponential GARCH (EGARCH) model, and the Glosten, Jagannathan, and Runkle (GJR) model.
Book Synopsis Time Series Analysis With Matlab by : Mara Prez
Download or read book Time Series Analysis With Matlab written by Mara Prez and published by CreateSpace. This book was released on 2014-09-12 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics:Conditional Mean Models for Stationary Processes Specify Conditional Mean Models Using ARIMA Autoregressive Model AR(p) Model AR Model with No Constant Term AR Model with Nonconsecutive Lags AR Model with Known Parameter Values AR Model with a t Innovation Distribution Moving Average Model MA(q) Model Invertibility of the MA Model MA Model Specifications MA Model with No Constant Term MA Model with Nonconsecutive Lags MA Model with Known Parameter Values MA Model with a t Innovation Distribution Autoregressive Moving Average ModelARMA(p,q) Model Stationarity and Invertibility of the ARMA Model ARMA Model Specifications ARMA Model with No Constant Term ARMA Model with Known Parameter Values ARIMA Model ARIMA Model Specifications ARIMA Model with Known Parameter Values Multiplicative ARIMA Model Multiplicative ARIMA Model Specifications Seasonal ARIMA Model with No Constant Term Seasonal ARIMA Model with Known Parameter Values Specify Multiplicative ARIMA Model ARIMA Model Including Exogenous Covariates ARIMAX(p,D,q) Model ARIMAX Model Specifications Specify Conditional Mean Model Innovation Distribution Specify Conditional Mean and Variance Model Impulse Response Function Plot Impulse Response Function Box-Jenkins Differencing vs ARIMA Estimation Maximum Likelihood Estimation for Conditional Mean ModelsConditional Mean Model Estimation with Equality Constraints Initial Values for Conditional Mean Model Estimation Optimization Settings for Conditional Mean Model Estimation Estimate Multiplicative ARIMA Model Model Seasonal Lag Effects Using Indicator Variables Forecast IGD Rate Using ARIMAX Model Estimate Conditional Mean and Variance Models Choose ARMA Lags Using BIC Infer Residuals for Diagnostic Checking Monte Carlo Simulation of Conditional Mean Models Presample Data for Conditional Mean Model Simulation Transient Effects in Conditional Mean Model Simulations Simulate Stationary Processes Simulate an AR Process Simulate an MA Process Simulate Trend-Stationary and Difference-Stationary Processes Simulate Multiplicative ARIMA Models Simulate Conditional Mean and Variance Models Monte Carlo Forecasting of Conditional Mean Models Monte Carlo Forecasts MMSE Forecasting of Conditional Mean Models Forecast Error Convergence of AR Forecasts Forecast Multiplicative ARIMA Model Forecast Conditional Mean and Variance Model
Book Synopsis Time Series Analysis With Matlab by : Mara Prez
Download or read book Time Series Analysis With Matlab written by Mara Prez and published by CreateSpace. This book was released on 2014-09-12 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics Conditional Variance Models GARCH Model Specify GARCH Models Using garch GARCH Model Specifications GARCH Model with a Mean Offset GARCH Model with Nonconsecutive Lags GARCH Model with Known Parameter Values GARCH Model with a t Innovation Distributio EGARCH Model Specify EGARCH Models Using egarch EGARCH Model Specifications EGARCH Model with a Mean Offset EGARCH Model with Nonconsecutive Lags EGARCH Model with Known Parameter Values EGARCH Model with a t Innovation Distribution GJR Model Specify GJR Models Using gjr GJR Model Specifications GJR Model with a Mean Offset GJR Model with Nonconsecutive Lags GJR Model with Known Parameter Values GJR Model with a t Innovation Distribution Modify Properties of Conditional Variance Model Objects Specify the Conditional Variance Model Innovation Distribution Specify a Conditional Variance Model Maximum Likelihood Estimation for Conditional Variance Models Innovation Distribution Loglikelihood Functions Conditional Variance Model Estimation with Equality Constraints Presample Data for Conditional Variance Model EstimationInitial Values for Conditional Variance Model Estimation Optimization Settings for Conditional Variance Model Estimation Conditional Variance Model Constraints Infer Conditional Variances and Residuals Likelihood Ratio Test for Conditional Variance Models Compare Conditional Variance Models Using Information Criteria Monte Carlo Simulation of Conditional Variance Models Presample Data for Conditional Variance Model Simulation Simulate GARCH Models Assess the EGARCH Forecast Bias Using Simulations Simulate Conditional Variance Model Monte Carlo Forecasting of Conditional Variance Models MMSE Forecasting of Conditional Variance Models EGARCH MMSE Forecasts Forecast GJR Models Forecast Conditional Variance Model Including an Exogenous Regression Component ARMAX Model Specifying ARMAX Models Using garchset Maximum Likelihood Estimation Initial Parameter Values for Optimization GARCHFIT Examples Estimation Presample Data GARCHSIM Examples Simulation Presample Data MMSE Forecasting GARCHPRED Examples
Book Synopsis Univariate Time Series Analysis With Matlab by : Mara Prez
Download or read book Univariate Time Series Analysis With Matlab written by Mara Prez and published by CreateSpace. This book was released on 2014-09-12 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics:Econometric Modeling Model Objects, Properties, and Methods Stochastic Process Characteristics Stationary Processes Linear Time Series Model Lag Operator Notation Unit Root ProcessNonstationary Processes Trend Stationary Difference Stationary Nonseasonal and Seasonal Differencing Time Series Decomposition Moving Average Filter Moving Average Trend Estimation Parametric Trend Estimation Hodrick-Prescott Filter Seasonal Filters Seasonal Adjustment Box-Jenkins Methodology Autocorrelation and Partial Autocorrelation Ljung-Box Q-Test Detect Autocorrelation Engle's ARCH Test Detect ARCH Effects Test Autocorrelation of Squared Residuals Engle's ARCH Test Unit Root Nonstationarity Modeling Unit Root Processes Testing for Unit Roots Test Simulated Data for a Unit RootAssess Stationarity of a Time Series Test Multiple Time Series Information Criteria Model Comparison Tests Likelihood Ratio Test Lagrange Multiplier Test Wald Test Covariance Matrix Estimation Compare GARCH Models Using Likelihood Ratio Test Check Fit of Multiplicative ARIMA Model Goodness of Fit Residual Diagnostics Check Residuals for Normality Check Residuals for Autocorrelation Check Residuals for Conditional Heteroscedasticity Check Predictive Performance Nonspherical Models Plot Confidence Band Using HAC Estimates Change the Bandwidth of a HAC Estimator
Book Synopsis Time Series Analysis by : George E. P. Box
Download or read book Time Series Analysis written by George E. P. Box and published by John Wiley & Sons. This book was released on 2015-05-29 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis New Introduction to Multiple Time Series Analysis by : Helmut Lütkepohl
Download or read book New Introduction to Multiple Time Series Analysis written by Helmut Lütkepohl and published by Springer Science & Business Media. This book was released on 2007-07-26 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Multivariate Tests for Time Series Models by : Jeff B. Cromwell
Download or read book Multivariate Tests for Time Series Models written by Jeff B. Cromwell and published by SAGE. This book was released on 1994 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests.
