1.

Record Nr.

UNINA9910438069303321

Autore

Kirchgässner Gebhard

Titolo

Introduction to Modern Time Series Analysis / / by Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler

Pubbl/distr/stampa

Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013

ISBN

3-642-33436-9

Edizione

[2nd ed. 2013.]

Descrizione fisica

1 online resource (325 p.)

Collana

Springer Texts in Business and Economics, , 2192-4333

Disciplina

519.55

Soggetti

Econometrics

Statistics 

Game theory

Macroeconomics

Statistics for Business, Management, Economics, Finance, Insurance

Game Theory, Economics, Social and Behav. Sciences

Macroeconomics/Monetary Economics//Financial Economics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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

Sommario/riassunto

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.  .