Applied Time Series Econometrics

A demonstration of how time series econometrics can be used in economics and finance.

Helmut Lütkepohl (Edited by), Markus Krätzig (Edited by)

9780521839198, Cambridge University Press

Hardback, published 2 August 2004

352 pages, 69 b/w illus. 38 tables
23.8 x 15.7 x 2.4 cm, 0.6 kg

Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.

Preface
Notation and abbreviations
List of contributors
Part I. Initial Tasks and Overview Helmut Lütkepohl: 1. Introduction
2. Setting up an econometric project
3. Getting data
4. Data handling
5. Outline of chapters
Part II. Univariate Time Series Analysis Helmut Lütkepohl: 6. Characteristics of time series
7. Stationary and integrated stochastic processes
8. Some popular time series models
9. Parameter estimation
10. Model specification
11. Model checking
12. Unit root tests
13. Forecasting univariate time series
14. Examples
15. Where to go from here
Part III. Vector Autoregressive and Vector Error Correction Models Helmut Lütkepohl: 16. Introduction
17. VARs and VECMs
18. Estimation
19. Model specification
20. Model checking
21. Forecasting VAR processes and VECMs
22. Granger-causality analysis
23. An example
24. Extensions
Part IV. Structural Vector Autoregressive Modelling and Impulse Responses Jörg Breitung, Ralf Brüggemann and Helmut Lütkepohl: 25. Introduction
26. The models
27. Impulse response analysis
28. Estimation of structural parameters
29. Statistical inference for impulse responses
30. Forecast error variance decomposition
31. Examples
32. Conclusions
Part V. Conditional Heteroskedasticity Helmut Herwartz: 33. Stylized facts of empirical price processes
34. Univariate GARCH models
35. Multivariate GARCH models
Part VI. Smooth Transition Regression Modelling Timo Teräsvirta: 36. Introduction
37. The model
38. The modelling cycle
39. Two empirical examples
40. Final remarks
Part VII. Nonparametric Time Series Modelling Rolf Tschernig: 41. Introduction
42. Local linear estimation
43. Bandwidth and lag selection
44. Diagnostics
45. Modelling the conditional volatility
46. Local linear seasonal modelling
47. Example I: average weekly working hours in the United States
48. Example II: XETRA dax index
Part VIII. The Software JMulTi Markus Krätzig: 49. Introduction to JMulTi
50. Numbers, dates and variables in JMulTi
51. Handling data sets
52. Selecting, transforming and creating time series
53. Managing variables in JMulTi
54. Notes for econometric software developers
55. Conclusion
References
Index.

Subject Areas: Statistical physics [PHS], Probability & statistics [PBT], Business mathematics & systems [KJQ], Finance [KFF], Economic statistics [KCHS], Econometrics [KCH]