Helmut Lütkepohl / Markus Krätzig (eds.)

Applied Time Series Econometrics

Herausgeber: Kratzig, Markus; Lutkepohl, Helmut

• Gebundenes Buch

Produktbeschreibung

Time series econometrics is used for predicting future developments of variables of interest such as economic growth, stock market volatility or interest rates. A model has to be constructed, accordingly, to describe the data generation process and to estimate its parameters. Modern tools to accomplish these tasks are provided in this volume, which also demonstrates by example how the tools can be applied.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 352
• Erscheinungstermin: 10. Juli 2004
• Englisch
• Abmessung: 235mm x 157mm x 25mm
• Gewicht: 722g
• ISBN-13: 9780521839198
• ISBN-10: 052183919X
• Artikelnr.: 21055695

Autorenporträt

Helmut Lütkepohl is Professor of Economics at the European University Institute in Florence, Italy. He is on leave from Humboldt University Berlin where he has been Professor of Econometrics in the Faculty of Economics and Business Administration since 1992. He had previously been Professor of Statistics at the University of Kiel (1987-1992) and the University of Hamburg (1985-1987) and was Visiting Assistant Professor at the University of California, San Diego (1984-85). Professor Lütkepohl is Associate Editor of Econometric Theory, the Journal of Applied Econometrics, Macroeconomic Dynamics, Empirical Economics and Econometric Reviewa. He has published extensively in learned journals and books and is author, co-author and editor of a number of books in econometrics and time series analysis. Professor Lütkepohl is the author of Introduction to Multiple Time Series Analysis (1991) and a Handbook of Matrices (1996). His current teaching and research interests include methodological issues related to the study of nonstationary, integrated time series and the analysis of the transmission mechanism of monetary policy in the Euro area.
Markus Krätzig is a doctoral student in the Department of Economics at Humboldt University, Berlin.

Inhaltsangabe

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.

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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.