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pelagatti matteo m. - time series modelling with unobserved components

Time Series Modelling with Unobserved Components




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Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 08/2015
Edizione: 1° edizione





Note Editore

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.




Sommario

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 InterventionsStatic regressionRegressors in components and dynamic regression Regression with time-varying coefficients EstimationThe 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




Autore

Matteo M. Pelagatti










Altre Informazioni

ISBN:

9781482225006

Condizione: Nuovo
Dimensioni: 9.25 x 6.25 in Ø 1.48 lb
Formato: Copertina rigida
Illustration Notes:63 b/w images and 7 tables
Pagine Arabe: 275


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