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Time Series Models for Business and Economic Forecasting

Time Series Models for Business and Economic Forecasting


  • Date Published: November 1998
  • availability: Replaced by 9780521520911
  • format: Paperback
  • isbn: 9780521586412


Replaced by 9780521520911
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About the Authors
  • Time Series Models for Business and Economic Forecasting is the most up-to-date and accessible guide to one of the fastest growing areas in business and economic analysis. The author is regarded as one of the most accomplished econometricians in Europe and this book is based on his highly successful lecture program for multidisciplinary, graduate and upper level undergraduate students. Early chapters of the book focus on the typical features of time series data in business and economics. Later chapters are concerned with the discussion of some important concepts in time series analysis, the techniques that can be readily applied in practice, different modeling methods and model structures, multivariate time, and the common aspects across time series.

    • Philip Franses is a rising star within econometrics teaching and research, this textbook is based around his highly successful lecture programme
    • The follow up book to two very successful CUP books in this area (MILLS/The Econometric Modelling of Financial Time Series; HARVEY/The Forecasting of Structural Time Series)
    • An easy to follow, up-to-date exposition including numerous examples and case studies, making this the most accessible book in this area, and the best starting point for nonspecialists
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    Product details

    • Date Published: November 1998
    • format: Paperback
    • isbn: 9780521586412
    • length: 292 pages
    • dimensions: 229 x 152 x 16 mm
    • weight: 0.4kg
    • contains: 29 tables
    • availability: Replaced by 9780521520911
  • 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.

  • Author

    Philip Hans Franses, Erasmus Universiteit Rotterdam

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