Sprache: Englisch
Verlag: Cambridge University Press, 2001
ISBN 10: 052156588X ISBN 13: 9780521565882
Anbieter: World of Books (was SecondSale), Montgomery, IL, USA
Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
Sprache: Englisch
Verlag: Cambridge University Press, Cambridge, London, 2010
ISBN 10: 052156588X ISBN 13: 9780521565882
Anbieter: Der Ziegelbrenner - Medienversand, Bremen, Deutschland
sehr gut erh/ very good condition, 228 S., Paperback, kart. Mit einem Vorwort des Wirtschafts-Nobelpreisträgers 2011, T.J. Sargent. themes in modern econometrics Gramm 600.
Sprache: Englisch
Verlag: Cambridge University Press, 2001
ISBN 10: 052156588X ISBN 13: 9780521565882
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 56,30
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Cambridge University Press, 2001
ISBN 10: 052156588X ISBN 13: 9780521565882
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. The treatment offers a thorough review of developments in econometric analysis of seasonal time series. Series: Themes in Modern Econometrics. Num Pages: 252 pages, 15 b/w illus. 2 tables. BIC Classification: KCH. Category: (P) Professional & Vocational. Dimension: 229 x 153 x 15. Weight in Grams: 352. . 2010. Illustrated. paperback. . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Cambridge University Press, 2001
ISBN 10: 052156588X ISBN 13: 9780521565882
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series, summarizing a decade of theoretical advances in the area. The authors discuss the asymptotic distribution theory for linear nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. Moreover, a comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.