Time Series Models for Business and Economic Forecasting

Franses, Philip Hans

ISBN 10: 0521586410 / ISBN 13: 9780521586412

Edité par Cambridge University Press, 2001

A propos de cet article

280 numbered pages; some highlighting in text; a few minor marks to edges of closed pages; light wear to corners and edges of mostly orange softcover; a couple minor creases to cover; several minor scratches/ minor dings to cover; weighs 1 pound and 1 ounce; measures 8.9 by 6.1 by .75 inches; 070123; The author is regarded as one of the most accomplished econometricians in Europe and this book is based on his highly successful lecture program for multidisciplinary, graduate and upper level undergraduate students.

N° de réf. du vendeur 20067

Détails bibliographiques

• Titre: Time Series Models for Business and Economic Forecasting
• Éditeur: Cambridge University Press
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 0521586410
• ISBN à 13 chiffres: 9780521586412
• Reliure: Soft cover
• État de l'article: Good
• Catalogues du vendeur:
  • Economics
  • Business

A propos de ce titre

Synopsis

An introduction to time series models for business and economic forecasting.

Présentation de l'éditeur

The econometric analysis of economic and business time series is a major field of research and application. The last few decades have witnessed an increasing interest in both theoretical and empirical developments in constructing time series models and in their important application in forecasting. In Time Series Models for Business and Economic Forecasting, Philip Franses examines recent developments in time series analysis. The early parts of the book focus on the typical features of time series data in business and economics. Part III is concerned with the discussion of some important concepts in time series analysis, the discussion focuses on the techniques which can be readily applied in practice. Parts IV-VIII suggest different modeling methods and model structures. Part IX extends the concepts in chapter three to multivariate time series. Part X examines common aspects across time series.