Time Series Models for Business and Economic Forecasting

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.

(Cambridge)

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'Franses reviews the more recent developments in modeling time series to focus on generating ex ante forecasts: seasonal unit roots, period models, aberrant observations, and common features. For each method, intuitive motivation and practical considerations are discussed in detail, making the book very readable ... should be beneficial for students and instructors of applications-oriented courses as well as for practitioners who wish to obtain a first, but not too technical, impression of time series forecasting using modern , recently developed methods.' Journal of the American Statistical Association

(Cambridge)

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.

(Cambridge)