Applied Time Series Econometrics

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. 

Inhaltsverzeichnis

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. 

Autoreninfo

Lütkepohl, Helmut
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.