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Applied Time Series Econometrics

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ISBN-10: 0521547873

ISBN-13: 9780521547871

Edition: 2004

Authors: Helmut L�tkepohl, Markus Kr�tzig, Peter C. B. Phillips, Christian Gourieroux, Michael Wickens

List price: $58.99
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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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Book details

List price: $58.99
Copyright year: 2004
Publisher: Cambridge University Press
Publication date: 8/4/2004
Binding: Paperback
Pages: 352
Size: 6.00" wide x 9.00" long x 1.00" tall
Weight: 1.056
Language: English

Markus Kr�tzig is a doctoral student in the Department of Economics at Humboldt University, Berlin.

Michael Wickens is professor of economics at the University Michael Wickens is professor of economics at the University of York. He is the coeditor of "Handbook of Applied Econometof York. He is the coeditor of "Handbook of Applied Econometrics" and was managing editor of the" Economic Journal" fromrics" and was managing editor of the" Economic Journal" from 1996 to 2004. 1996 to 2004.

Notation and abbreviations
List of contributors
Initial Tasks and Overview
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
Where to go from here
Vector Autoregressive and Vector Error Correction Models
VARs and VECMs
Model specification
Model checking
Forecasting VAR processes and VECMs
Granger-causality analysis
An example
Structural Vector Autoregressive Modelling and Impulse Responses
The models
Impulse response analysis
Estimation of structural parameters
Statistical inference for impulse responses
Forecast error variance decomposition
Conditional Heteroskedasticity
Stylized facts of empirical price processes
Univariate GARCH models
Multivariate GARCH models
Smooth Transition Regression Modelling
The model
The modelling cycle
Two empirical examples
Final remarks
Nonparametric Time Series Modelling
Local linear estimation
Bandwidth and lag selection
Modelling the conditional volatility
Local linear seasonal modeling
Example I: average weekly working hours in the United States
Example II: XETRA dax
The Software
Introduction to JMulTi
Numbers, dates and variables in JMulTi
Handling data sets
Selecting, transforming and creating time series
Managing variables in JMulTi
Notes for econometric software developers