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
Biography
Matteo M. Pelagatti
"The main contribution of the book relative to existing books on this topic is that it emphasizes the actual model class, rather than methods for these kind of models. The author points out that despite the many advantages of this rich class, its use is still limited among practitioners. He hopes that his new angle will further popularize unobserved component models…the book really achieves its purpose and differentiates itself from alternatives, and is therefore a valuable addition and worth buying. The discussion of software in Chapter 10 is extremely timely and a great plus for practitioners and researchers that are ready to sit down and start implementing. For each software package, clear examples are given on how to run an example unobserved component model…In closing, the book reads well and really provides the reader with a broad understanding of the unobserved component approach. This includes models, methods, and the discussion of software packages. I can imagine that besides being relevant and interesting for practitioners, students will benefit from reading this book. I personally would be more than happy to suggest it to Master and advanced Bachelor students in Econometrics working on the topic in a course or for their thesis."
—Michel van der Wel, Erasmus University Rotterdam, The American Statistician, November 2016"Overall, this is a unique book on time series analysis in that it covers substantial amount of material lucidly and succinctly without much fluff in less than 260 pages and achieves its five stated goals. I enjoyed reading the book, and I believe it is an excellent reference book for UCM and related software packages, time series analysis, and study of business cycles. It can also be used as a companion for teaching time series analysis along with a standard time series text."
—Journal of Time Series Analysis, 2016
