Multiple Time Series Models

Patrick T. Brandt
John Taylor Williams
Quantitative Applications in the Social Sciences
Multiple Time Series Models
September 2006 | 120 pages | Sage US
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Description

Many analyses of time series data involve multiple, related variables.  Multiple Time Series Models presents many specification choices and special challenges.  This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression.  The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned.  Specification, estimation, and inference using these models is discussed.  The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.

Key Features

  • Offers a detailed comparison of different time series methods and approaches.
  • Includes a self-contained introduction to vector autoregression modeling.
  • Situates multiple time series modeling as a natural extension of commonly taught statistical models.

Learn more about "The Little Green Book" - QASS Series! Click Here



Contents

List of Figures

List of Figures

List of Tables

List of Tables

Series Editor?s Introduction

Series Editor?s Introduction

Preface

  • 1. Introduction to Multiple Time Series Models
  • 1.1 Simultaneous Equation Approach
  • 1.2 ARIMA Approach
  • 1.3 Error Correction or LSE Approach
  • 1.4 Vector Autoregression Approach
  • 1.5 Comparison and Summary
  • 2. Basic Vector Autoregression Models
  • 2.1 Dynamic Structural Equation Models
  • 2.2 Reduced Form Vector Autoregressions
  • 2.3 Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model
  • 2.4 Working With This Model
  • 2.5 Specification and Analysis of VAR Models
  • 2.6 Other Specification Issues
  • 2.7 Unit Roots and Error Correction in VARs
  • 2.8 Criticisms of VAR
  • 3. Examples of VAR Analyses
  • 3.1 Public Mood and Macropartisanship
  • 3.2 Effective Corporate Tax Rates
  • 3.3 Conclusion

Appendix: Software for Multiple Time Series Models

Appendix: Software for Multiple Time Series Models

Notes

Notes

References

References

Index

Index

About the Authors

About the Authors

Additional materials

Description

Many analyses of time series data involve multiple, related variables.  Multiple Time Series Models presents many specification choices and special challenges.  This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression.  The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned.  Specification, estimation, and inference using these models is discussed.  The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.

Key Features

  • Offers a detailed comparison of different time series methods and approaches.
  • Includes a self-contained introduction to vector autoregression modeling.
  • Situates multiple time series modeling as a natural extension of commonly taught statistical models.

Learn more about "The Little Green Book" - QASS Series! Click Here



Contents

List of Figures

List of Figures

List of Tables

List of Tables

Series Editor?s Introduction

Series Editor?s Introduction

Preface

  • 1. Introduction to Multiple Time Series Models
  • 1.1 Simultaneous Equation Approach
  • 1.2 ARIMA Approach
  • 1.3 Error Correction or LSE Approach
  • 1.4 Vector Autoregression Approach
  • 1.5 Comparison and Summary
  • 2. Basic Vector Autoregression Models
  • 2.1 Dynamic Structural Equation Models
  • 2.2 Reduced Form Vector Autoregressions
  • 2.3 Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model
  • 2.4 Working With This Model
  • 2.5 Specification and Analysis of VAR Models
  • 2.6 Other Specification Issues
  • 2.7 Unit Roots and Error Correction in VARs
  • 2.8 Criticisms of VAR
  • 3. Examples of VAR Analyses
  • 3.1 Public Mood and Macropartisanship
  • 3.2 Effective Corporate Tax Rates
  • 3.3 Conclusion

Appendix: Software for Multiple Time Series Models

Appendix: Software for Multiple Time Series Models

Notes

Notes

References

References

Index

Index

About the Authors

About the Authors

Additional materials

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Multiple Time Series Models

Patrick T. Brandt , John Taylor Williams
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September 2006 | 120 pages | Sage US

Format Published Date ISBN Price

Many analyses of time series data involve multiple, related variables.  Multiple Time Series Models presents many specification choices and special challenges.  This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression.  The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned.  Specification, estimation, and inference using these models is discussed.  The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.

Key Features

  • Offers a detailed comparison of different time series methods and approaches.
  • Includes a self-contained introduction to vector autoregression modeling.
  • Situates multiple time series modeling as a natural extension of commonly taught statistical models.

Learn more about "The Little Green Book" - QASS Series! Click Here




Table Of Contents:

  • List of Figures
  • List of Tables
  • Series Editor?s Introduction
  • Preface
  • 1. Introduction to Multiple Time Series Models
  • 1.1 Simultaneous Equation Approach
  • 1.2 ARIMA Approach
  • 1.3 Error Correction or LSE Approach
  • 1.4 Vector Autoregression Approach
  • 1.5 Comparison and Summary
  • 2. Basic Vector Autoregression Models
  • 2.1 Dynamic Structural Equation Models
  • 2.2 Reduced Form Vector Autoregressions
  • 2.3 Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model
  • 2.4 Working With This Model
  • 2.5 Specification and Analysis of VAR Models
  • 2.6 Other Specification Issues
  • 2.7 Unit Roots and Error Correction in VARs
  • 2.8 Criticisms of VAR
  • 3. Examples of VAR Analyses
  • 3.1 Public Mood and Macropartisanship
  • 3.2 Effective Corporate Tax Rates
  • 3.3 Conclusion
  • Appendix: Software for Multiple Time Series Models
  • Notes
  • References
  • Index
  • About the Authors

Recent Product Reviews:

"This book amazingly introduces multiple time series on varied levels to help the reader to understand their assumptions, their four approaches, how to build theories to accompany their modeling, and how to interpret their results. This book would be quite an initiation, sweet and succinct, in advanced undergraduate and graduate courses on time series. In addition, it is a useful and reliable resource . . . this book also makes a fun reading!"
Ruth Chao

Recommendations