Applied Time Series Econometrics

by Helmut Lütkepohl, Markus Krätzig

Overview

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.

Product Details

ISBN-13:	9780521547871
Publisher:	Cambridge University Press
Publication date:	08/04/2004
Series:	Themes in Modern Econometrics
Edition description:	New Edition
Pages:	352
Product dimensions:	5.98(w) x 9.02(h) x 0.79(d)

About the Author

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.

Table of Contents

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.