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MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS

MULTIVARIATE TIME SERIES ANALYSIS with MATLAB. VAR and VARMAX MODELS
Author: Perez M.
Publisher: Createspace Independent Publishing Platform
Total Pages: 176
Release: 2016-06-24
Genre:
ISBN: 9781534868076

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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


Multivariate Time Series Analysis With Matlab

Multivariate Time Series Analysis With Matlab
Author: Mara Prez
Publisher: CreateSpace
Total Pages: 176
Release: 2014-09-12
Genre: Mathematics
ISBN: 9781502348579

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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


Linear Time Series with MATLAB and OCTAVE

Linear Time Series with MATLAB and OCTAVE
Author: Víctor Gómez
Publisher: Springer Nature
Total Pages: 355
Release: 2019-10-04
Genre: Computers
ISBN: 3030207900

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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.


Econometric With Matlab

Econometric With Matlab
Author: A. Smith
Publisher:
Total Pages: 194
Release: 2017-11-10
Genre:
ISBN: 9781979619684

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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


Multivariate Time Series Analysis

Multivariate Time Series Analysis
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
Total Pages: 414
Release: 2013-11-11
Genre: Mathematics
ISBN: 1118617754

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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.


Multiple Time Series Modeling Using the SAS VARMAX Procedure

Multiple Time Series Modeling Using the SAS VARMAX Procedure
Author: Anders Milhoj
Publisher: SAS Institute
Total Pages: 210
Release: 2016-01-11
Genre: Computers
ISBN: 162959749X

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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. --


Time Series Analysis with Matlab. Arima and Arimax Models

Time Series Analysis with Matlab. Arima and Arimax Models
Author: Perez M.
Publisher: Createspace Independent Publishing Platform
Total Pages: 192
Release: 2016-06-23
Genre:
ISBN: 9781534860919

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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


Univariate Time Series Analysis with MATLAB

Univariate Time Series Analysis with MATLAB
Author: Perez M.
Publisher: Createspace Independent Publishing Platform
Total Pages: 222
Release: 2016-06-26
Genre:
ISBN: 9781534867062

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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.


Time Series Analysis with MATLAB. Arima/Varmax/Garch/Gjr Models. Functions and Examples

Time Series Analysis with MATLAB. Arima/Varmax/Garch/Gjr Models. Functions and Examples
Author: Karter J
Publisher: Createspace Independent Publishing Platform
Total Pages:
Release: 2016-10-15
Genre:
ISBN: 9781539546382

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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.


Multivariate Time Series Analysis and Applications

Multivariate Time Series Analysis and Applications
Author: William W. S. Wei
Publisher: John Wiley & Sons
Total Pages: 536
Release: 2019-03-18
Genre: Mathematics
ISBN: 1119502853

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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.