XWe have detected your location as outside the U.S/Canada, if you think this is wrong, you can choose your location.

Macmillan Higher Education Celebrating 20 years of Macmillan Study Skills

Cart

Continue Shopping
All prices are shown excluding Tax
The submitted promocode is invalid
Discount code already used. It can only be used once.
* Applied promocode: ×

Important information on your ebook order

Introduction to Modern Time Series Analysis (2nd Edition)

Author(s):
Publisher:

Springer

Pages: 320
Further Actions:

Recommend to library

AVAILABLE FORMATS

Paperback - 9783642440298

09 November 2014

$99.00

In stock

Ebook - 9783642334368

08 October 2012

$74.99

In stock

All prices are shown excluding Tax

This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important...

Show More

This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.

 

Show Less

Presents modern methods of time series econometrics and their applications to macroeconomics and finance

With numerous examples and analyses based on real economic data

Helps to acquire a rigorous understanding of the methods and to develop empirical skills

Introduction and Basics
Univariate Stationary Processes
Granger Causality
Vector Autoregressive Processes
Nonstationary Processes
Cointegration
Nonstationary Panel Data
Autoregressive Conditional Heteroscedasticity.
Add a review

New Publications 

Best Sellers