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Unit information: Business and Economic Forecasting in 2020/21

Unit name Business and Economic Forecasting
Unit code EFIMM0109
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Crespo
Open unit status Not open
Pre-requisites

EFIM10020 or equivalent courses.

Co-requisites

None.

School/department School of Economics
Faculty Faculty of Social Sciences and Law

Description

Forecasting is an integral part of the decision-making activities in business, finance, economics, government, and many other fields. In recent years there have been extensive developments in the methods used in forecasting. In this course we look at different approaches and techniques that help decision makers make the best possible judgments about future events. The course will normally cover forecasting with regression models, stationary and non-stationary time series models and forecasting, autoregressive conditional heteroskedasticity models and vector autoregressive models. The unit will discuss data acquisition as well as data handling. The course will enable students to be better prepared for dissertations, which might require the analysis of time series data. The techniques covered are widely used in Economics, Finance and Management, and a knowledge of them will also enable students to be better able to apply them in their future work. Students selecting this unit should have basic/introductory knowledge in statistics and/or econometrics.

Unit aims:

(1) To provide the core concepts in time series techniques, which are used in Economics and Finance.

(2) Using real-world cases, the unit will enable students to identify appropriate forecasting models for a variety of situations.

(3) To prepare students for the dissertations, which might require the analysis of time series data.

Intended learning outcomes

At the end of the course a successful student will be able to: (1) estimate time series models and employ them to make forecasts; (2) to be proficient in the application of different forecasting methodologies in order to use them in their future academic or professional career; (3) to recognize the importance of the concept of non-stationarity, its consequences and how to test for it.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities

Assessment Details

Coursework (85%) and MCQ Test (15%).

Reading and References

Wilson, H.J. and B. Keating (2009), Business Forecasting, 6th edition, McGraw-Hill.

Ord, K. and R. Fildes (2013), Principles of Business Forecasting, International Edition, South-Western.

Hanke, J. E. and D. W. Wichern (2009), Business Forecasting, 9th edition, Pearson Education.

Diebold, F. (2007), Elements of Forecasting, 4th edition, Thomson South-Western.

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