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Quick Introduction to SSMMATLAB Read chapter Read first chapter

2019 | OriginalPaper | Chapter

8. Cointegrated VARMA Models

Author : Víctor Gómez

Published in: Linear Time Series with MATLAB and OCTAVE

Publisher: Springer International Publishing

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Abstract

In this section, it is briefly shown how cointegrated VARMA models can be handled in SSMMATLAB. The user can consult Gómez (Multivariate time series models with linear state space structure. Springer, New York, 2016, Sect. 5.7) for more information on the subject. The VARMA models can be ordinary, multiplicative as in Sect. 2.​1.​5, or in echelon form as in Sect. 3.​1.​6. The following discussion is valid for all these types of models. Later, we will specify the different functions appropriate for each model.

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previous chapter Multivariate Structural Models
next chapter Simulation of Common Univariate and Multivariate Models
Literature
Gómez, V. (2013). A strongly consistent criterion to decide between I(1) and I(0) processes based on different convergence rates. Communications in Statistics-Simulation and Computation,42, 1848–1864.MathSciNetMATH
Gómez, V. (2016). Multivariate time series models with linear state space structure. New York: Springer.MATH
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin: Springer.CrossRef
Reinsel, G. C.(1997). Elements of multivariate time series analysis. New York: Springer.CrossRef
Tsay, R. S. (2014). Multivariate time series analysis with R and financial applications. New York: Wiley.MATH
Metadata
Title
Cointegrated VARMA Models
Author
Víctor Gómez
Copyright Year
2019
Book
Linear Time Series with MATLAB and OCTAVE

Print ISBN: 978-3-030-20789-2

Electronic ISBN: 978-3-030-20790-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20790-8_8

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