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Franses / Dijk / Opschoor

Time Series Models for Business and Economic Forecasting

Medium: Buch
ISBN: 978-0-521-81770-7
Verlag: Cambridge University Press
Erscheinungstermin: 04.07.2014
Lieferfrist: bis zu 10 Tage

With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. A companion website with downloadable datasets, exercises and lecture slides rounds out the full learning package.


Produkteigenschaften


  • Artikelnummer: 9780521817707
  • Medium: Buch
  • ISBN: 978-0-521-81770-7
  • Verlag: Cambridge University Press
  • Erscheinungstermin: 04.07.2014
  • Sprache(n): Englisch
  • Auflage: 2. Auflage 2014
  • Produktform: Gebunden
  • Gewicht: 788 g
  • Seiten: 312
  • Format (B x H x T): 183 x 260 x 21 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

Philip Hans Franses is Professor of Applied Econometrics and Professor of Marketing Research at the Erasmus School of Economics.

Dick van Dijk is Professor of Financial Econometrics at the Erasmus School of Economics.

Anne Opschoor has recently completed a PhD at the Erasmus School of Economics and is an Assistant Professor at the Free University.

Preface; 1. Introduction and overview; 2. Key features of economic time series; 3. Useful concepts in univariate time series analysis; 4. Trends; 5. Seasonality; 6. Aberrant observations; 7. Conditional heteroskedasticity; 8. Non-linearity; 9. Multivariate time series; Index.