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Applied Time Series Econometrics, Paperback by Lutkepohl, Helmut (EDT); Kratz...
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Item specifics
- Condition
- Book Title
- Applied Time Series Econometrics
- ISBN
- 9780521547871
- Subject Area
- Mathematics, Business & Economics
- Publication Name
- Applied Time Series Econometrics
- Publisher
- Cambridge University Press
- Item Length
- 9 in
- Subject
- Probability & Statistics / Time Series, Econometrics
- Publication Year
- 2004
- Series
- Themes in Modern Econometrics Ser.
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 0.8 in
- Item Weight
- 17.3 Oz
- Item Width
- 6 in
- Number of Pages
- 352 Pages
About this product
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
0521547873
ISBN-13
9780521547871
eBay Product ID (ePID)
30202830
Product Key Features
Number of Pages
352 Pages
Publication Name
Applied Time Series Econometrics
Language
English
Subject
Probability & Statistics / Time Series, Econometrics
Publication Year
2004
Type
Textbook
Subject Area
Mathematics, Business & Economics
Series
Themes in Modern Econometrics Ser.
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Weight
17.3 Oz
Item Length
9 in
Item Width
6 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2003-068720
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
330/.01/51955
Table Of Content
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.
Synopsis
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., The cointegration revolution has had a substantial impact on applied analysis. The methods for conducting this analysis are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can be used as a textbook for courses on applied time series econometrics., 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.
LC Classification Number
HA30.3.A67 2004
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