Time series models for business and economic forecasting

Author(s)

Bibliographic Information

Time series models for business and economic forecasting

Philip Hans Franses

Cambridge University Press, 1998

  • : hbk
  • : pbk

Available at  / 71 libraries

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Note

Bibliography: p. 261-273

Includes indexes

Description and Table of Contents

Description

The econometric analysis of economic and business time series is a major field of research and application. The last few decades have witnessed an increasing interest in both theoretical and empirical developments in constructing time series models and in their important application in forecasting. In Time Series Models for Business and Economic Forecasting, Philip Franses examines recent developments in time series analysis. The early parts of the book focus on the typical features of time series data in business and economics. Part III is concerned with the discussion of some important concepts in time series analysis, the discussion focuses on the techniques which can be readily applied in practice. Parts IV-VIII suggest different modeling methods and model structures. Part IX extends the concepts in chapter three to multivariate time series. Part X examines common aspects across time series.

Table of Contents

  • Part I. Introduction
  • Part II. Key Features of Economic Time Series: 1. Trends
  • 2. Seasonality
  • 3. Aberrant observations
  • 4. Conditional heteroskedasticity
  • 5. Nonlinearity
  • 6. Common features
  • Part III. Useful Concepts in Univariate Time Series Analysis: 7. Autoregressive moving average models
  • 8. Autocorrelation and identification
  • 9. Estimation and diagnostic measures
  • 10. Model selection
  • 11. Forecasting
  • Part IV. Trends: 12. Modeling trends
  • 13. Testing for unit roots
  • 14. Testing for stationarity
  • 15. Forecasting
  • Part V. Seasonality: 16. Typical features of seasonal time series
  • 17. Seasonal unit roots
  • 18. Periodic models
  • 19. Miscellaneous topics
  • Part VI. Aberrant Observations: 20. Modeling aberrant observations
  • 21. Testing for aberrant observations
  • 22. Irregular data and unit roots
  • Part VII. Conditional Heteroskedasticity: 23. Models for heteroskedasticity
  • 24. Specification and forecasting
  • 25. Various extensions
  • Part VIII. Nonlinearity: 26. Some models and their properties
  • 27. Empirical specification strategy
  • Part IX. Multivariate Time Series: 28. Representations
  • 29. Empirical model building
  • 30. Use of VAR models
  • Part X. Common Features: 31. Some preliminaries for a bivariate time series
  • 32. Common trends and co-integration
  • 33. Common seasonality and other features
  • Data appendix.

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