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Time Series Modelling with Unobserved Components

Time Series Modelling with Unobserved Components

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  • ISBN:9781482225006
  • Editorial: Taylor & Francis
  • Fecha de la edición:
  • Encuadernación:
  • Nº Pág.:275
  • Idiomas:Ingles

Materias:

Unobserved Components Models (UCMs) are a special class of time series models that have many advantages compared with other models in that they tend to provide more accurate forecasts and can be easily implemented. This book provides an overview of time series modelling using UCMs with an emphasis on real-world applications and solutions to practical problems. Detailed worked examples, primarily from economics and business, provide additional guidance on the use of appropriate software for each method.

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

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