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Applied Time Series Econometrics : Themes in Modern Econometrics - Helmut Lutkepohl

Applied Time Series Econometrics

Themes in Modern Econometrics

By: Helmut Lutkepohl (Editor), Markus Kraetzig (Editor)

Hardcover

Published: 29th June 2007
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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.

Initial Tasks and Overview
Introduction
Setting up an econometric project
Getting data
Data handling
Outline of chapters
Univariate Time Series Analysis
Characteristics of time series
Stationary and integrated stochastic processes
Some popular time series models
Parameter estimation
Model specification
Model checking
Unit root tests
Forecasting univariate time series
Examples
Where to go from here?
Vector Autoregressive and Vector Error Correction Models
Introduction
VARs and VECMs
Estimation
Model specification
Model checking
Forecasting VAR processes and VECMs
Granger-causality analysis
An example
Extensions
Structural Vector Autoregressive Modelling and Impulse Responses
Introduction
The models
Impulse response analysis
Estimation of structural parameters
Statistical inference for impulse responses
Forecast error variance decomposition
Examples
Conclusions
Conditional Heteroskedasticity
Stylized facts of empirical price processes
Univariate GARCH models
Multivariate GARCH models
Smooth Transition Regression Modelling
Introduction
The model
The modelling cycle
Two empirical examples
Final remarks
Nonparametric Time Series Modelling
Introduction
Local linear estimation
Bandwidth and lag selection
Diagnostics
Modelling the conditional volatility
Local linear seasonal modelling
Example I: average weekly working hours in the U.S.
Example II: XETRA dax index
The Software JMulTi
Introduction to JMulTi
Numbers, dates and variables in JMulTi
Handling datasets
Selecting, transforming and creating time series
Managing variables in JMulTi
Notes for econometric software developers
Conclusion
Table of Contents provided by Publisher. All Rights Reserved.

ISBN: 9780521839198
ISBN-10: 052183919X
Series: Themes in Modern Econometrics
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 352
Published: 29th June 2007
Publisher: Cambridge University Press
Country of Publication: GB
Dimensions (cm): 22.8 x 15.2  x 2.4
Weight (kg): 0.59