Multivariate Methods and Forecasting with IBM® SPSS® Statistics (Statistics and Econometrics for Finance)

Aljandali, Abdulkader

ISBN 10: 3319564803 / ISBN 13: 9783319564807

Published by Springer, 2017

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Bibliographic Details

• Title: Multivariate Methods and Forecasting with IBM® SPSS® Statistics (Statistics and Econometrics for Finance)
• Publisher: Springer
• Publication Date: 2017
• Language: English
• ISBN 10: 3319564803
• ISBN 13: 9783319564807
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introduces multivariate regression models and provides examples to help understand theory underpinning the model. The book presents the fundamentals of multivariate regression and then moves on to examine several related techniques that have application in business-orientated fields such as logistic and multinomial regression. Forecasting tools such as the Box-Jenkins approach to time series modeling are introduced, as well as exponential smoothing and naïve techniques. This part also covers hot topics suchas Factor Analysis, Discriminant Analysis and Multidimensional Scaling (MDS).

About the Author

Abdulkader Aljandali, Ph.D., is Senior Lecturer at Regent’s University London. He currently leads the Business Forecasting and the Quantitative Finance module at Regent’s in addition to acting as a Visiting Professor for various universities across the UK, Germany and Morocco. Dr Aljandali is an established member of the Higher Education Academy (HEA) and an active member of the British Accounting and Finance Association (BAFA).