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Unit information: Introduction to Business and Economic Forecasting in 2017/18

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Unit name Introduction to Business and Economic Forecasting
Unit code ECONM2038
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

None.

Co-requisites

None.

School/department School of Economics, Finance and Management
Faculty Faculty of Social Sciences and Law

Description

This unit follows on from the Autumn Term unit in Quantitative Methods. It will be a topics based unit and will normally cover stationary time series models and forecasting, non-stationarity, structural models and vector autoregressive models. The unit will also discuss data acquisition and data handling. This will enable students to be better prepared for dissertations which 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.

Intended learning outcomes

1) The course will enable students to identify appropriate forecasting models for a variety of situations, and to be proficient in the application of different forecasting methods.

2) Students will learn how to estimate structural and time series models, and use them to make forecasts.

3) Students will understand the concept of non-stationarity, its consequences and how to test for it.

Teaching details

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Twelve (12) large-group lectures and (small-group) classes meeting five (5) times.

Assessment Details

Formative assessment (tests ILOs 1, 2 and 3):

This consists of an assignment in which the student is given a data set to estimate a forecasting model for a particular economic variable. The assignment evaluates the familiarity of the student with the econometric techniques taught in the course. Word length: 2000 words.

Summative assessment (tests ILOs 1, 2 and 3):

A two-hour written exam (100%). Questions will be set that test understanding of key concepts such as stationary process, unit roots and Box-Jenkins Methodology. A theoretical model will be provided so that students can demonstrate their ability to derive key parameters and/or provide an interpretation of results derived from time series analysis.

Reading and References

  • Diebold, F. Elements of Forecasting (4th edition), Thomson South-Western, 2007
  • Wilson, H.J. and B. Keating, Business Forecasting (5th edition), McGraw-Hill, 2007

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