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Bayesian Forecasting and Dynamic Models

  • Textbook
  • © 1997

Overview

Authors:
  1. Mike West

    1. Institute of Statistics and Decision Science, Duke University, Durham, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Jeff Harrison

    1. Department of Statistics, University of Warwick, Coventry, England

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction

    Pages 1-31

  3. Introduction to the DLM: The First-Order Polynomial Model

    Pages 32-67

  4. Introduction to the DLM: The Dynamic Regression Model

    Pages 68-96

  5. The Dynamic Linear Model

    Pages 97-142

  6. Univariate Time Series DLM Theory

    Pages 143-177

  7. Model Specification and Design

    Pages 178-207

  8. Polynomial Trend Models

    Pages 208-233

  9. Seasonal Models

    Pages 234-269

  10. Regression, Autoregression, and Related Models

    Pages 270-316

  11. Illustrations and Extensions of Standard DLMs

    Pages 317-368

  12. Intervention and Monitoring

    Pages 369-426

  13. Multi-Process Models

    Pages 427-488

  14. Non-Linear Dynamic Models: Analytic and Numerical Approximations

    Pages 489-515

  15. Exponential Family Dynamic Models

    Pages 516-560

  16. Simulation-Based Methods in Dynamic Models

    Pages 561-580

  17. Multivariate Modelling and Forecasting

    Pages 581-630

  18. Distribution Theory and Linear Algebra

    Pages 631-651

  19. Back Matter

    Pages 652-681
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Keywords

About this book

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 analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scienti?c, and socio-economic ?elds. Much of the technical - velopment has been driven by the needs of forecasting practitioners and applied researchers. As a result, there now exists a relatively complete statistical and mathematical framework, presented and illustrated here. In writing and revising this book, our primary goals have been to present a reasonably comprehensive view of Bayesian ideas and methods in m- elling and forecasting, particularly to provide a solid reference source for advanced university students and research workers.

Authors and Affiliations

  • Institute of Statistics and Decision Science, Duke University, Durham, USA

    Mike West

  • Department of Statistics, University of Warwick, Coventry, England

    Jeff Harrison

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