Linear Time Series with MATLAB and Octave

ISBN-13: 9783030207892 / Angielski / Twarda / 2019 / 339 str.

Victor Gomez

Spis treści

Preface.- Software Installation.- Stationarity, VARMA and ARIMA Models.- VARMAX and Transfer Function Models.- Unobserved Components in Univariate Series.- Spectral Analysis.- Computing Echelon Forms by Polynomial Methods.- Multivariate Structural Models.- Cointegrated VARMA Models.- Simulation of Common Univariate and Multivariate Models.- The State Space Model.- SSMMATLAB Examples by Subject.- Author Index.- Subject Index.

O autorze

Dr. Víctor Gómez is a statistician and technical advisor at the Spanish Ministry of Finance and Public Administrations in Madrid. His work involves statistical, econometric and, above all, time series analysis of macroeconomic data, mostly in connection with short-term economic analysis. More recently, he has focused on research in the field of time series analysis and the development of software for time series analysis. He has also taught numerous courses on time series analysis and related topics such as short-term forecasting, seasonal adjustment methods and time series filtering.

 

Blurb

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.