Applied Time Series Econometrics Lütkepohl Krätzig Hardback 9780521839198

Description: 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 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]

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