Pelagatti Time Series Modelling with Unobserved Components

1. Auflage 2015
ISBN: 978-1-4822-2501-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 275 Seiten

Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community. Time Series Modelling with Unobserved Components rectifies this deficiency by giving a practical overview of the UCM approach, covering some theoretical details, several applications, and the software for implementing UCMs.

The book’s first part discusses introductory time series and prediction theory. Unlike most other books on time series, this text includes a chapter on prediction at the beginning because the problem of predicting is not limited to the field of time series analysis.

The second part introduces the UCM, the state space form, and related algorithms. It also provides practical modeling strategies to build and select the UCM that best fits the needs of time series analysts.

The third part presents real-world applications, with a chapter focusing on business cycle analysis and the construction of band-pass filters using UCMs. The book also reviews software packages that offer ready-to-use procedures for UCMs as well as systems popular among statisticians and econometricians that allow general estimation of models in state space form.

This book demonstrates the numerous benefits of using UCMs to model time series data. UCMs are simple to specify, their results are easy to visualize and communicate to non-specialists, and their forecasting performance is competitive. Moreover, various types of outliers can easily be identified, missing values are effortlessly managed, and working contemporaneously with time series observed at different frequencies poses no problem.

Autoren/Hrsg.

Pelagatti, Matteo Maria

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

STATISTICAL PREDICTION AND TIME SERIES

Statistical Prediction

Optimal predictor

Optimal linear predictor

Linear models and joint normality

Time Series Concepts

Definitions

Stationary processes

Integrated processes

ARIMA models

Multivariate extensions

UNOBSERVED COMPONENTS

Unobserved Components Model

The unobserved components model

Trend

Cycle

Seasonality

Regressors and Interventions
Static regression
Regressors in components and dynamic regression

Regression with time-varying coefficients

Estimation
The state space form

Models in state space form

Inference for the unobserved components

Inference for the unknown parameters

Modelling

Transforms

Choosing the components

State space form and estimation

Diagnostics checks, outliers and structural breaks

Model selection

Multivariate Models

Trends

Cycles

Seasonalities

State space form and parametrisation

APPLICATIONS

Business Cycle Analysis with UCM

Introduction to the spectral analysis of time series

Extracting the business cycle from one time series

Extracting the business cycle from a pool of time series

Case Studies

Impact of the point system on road injuries in Italy

An example of benchmarking: Building monthly GDP data

Hourly electricity demand

Software for UCM

Software with ready-to-use UCM procedures

Software for generic models in state space form