Bayesian Forecasting and Dynamic Models

• Main Authors: West, Mike (Author), Harrison, Jeff (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 1997.
• Edition: Second Edition.
• Series: Springer Series in Statistics,
• Subjects: Mathematics.; Probabilities.; Economic theory.; Probability Theory and Stochastic Processes.; Economic Theory/Quantitative Economics/Mathematical Methods.

This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time - ries analy...

Table of Contents:

• to the DLM: The First-Order Polynomial Model
• to the DLM: The Dynamic Regression Model
• The Dynamic Linear Model
• Univariate Time Series DLM Theory
• Model Specification and Design
• Polynomial Trend Models
• Seasonal Models
• Regression, Autoregression, and Related Models
• Illustrations and Extensions of Standard DLMs
• Intervention and Monitoring
• Multi-Process Models
• Non-Linear Dynamic Models: Analytic and Numerical Approximations
• Exponential Family Dynamic Models
• Simulation-Based Methods in Dynamic Models
• Multivariate Modelling and Forecasting
• Distribution Theory and Linear Algebra.