1.

Record Nr.

UNINA9910457599103321

Titolo

Applied time series econometrics / / edited by Helmut Lütkepohl, Markus Krätzig [[electronic resource]]

Pubbl/distr/stampa

Cambridge : , : Cambridge University Press, , 2004

ISBN

1-107-71373-0

1-280-54116-4

1-139-13080-3

0-511-21560-6

0-511-21739-0

0-511-21202-X

0-511-60688-5

0-511-21379-4

Descrizione fisica

1 online resource (xxv, 323 pages) : digital, PDF file(s)

Collana

Themes in modern econometrics

Disciplina

330/.01/51955

Soggetti

Time-series analysis - Mathematical models

Econometrics

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Nota di bibliografia

Includes bibliographical references (p. 301-315) and index.

Nota di contenuto

Initial tasks and overview ; Univariate time series analysis ; Vector autoregressive and vector error correction models / Helmut Lütkepohl -- Structural vector autoregressive modeling and impulse responses / Jörg Breitung, Ralf Brüggemann, and Helmut Lütkepohl -- Conditional heteroskedasticity / Helmut Herwartz -- Smooth transition regression modeling / Timo Teräsvirta -- Nonparametric time series modeling / Rolf Tschernig -- The software JMulTi / Markus Krätzig.

Sommario/riassunto

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.