Econometrics 2: Topics in Time Series - EC543

Location Term Level Credits (ECTS) Current Convenor 2018-19 2019-20
(version 2)
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6 15 (7.5) PROF H Krolzig


EC500 Microeconomics, EC502 Macroeconomics, EC580 Introduction to Econometrics, EC581 Introduction to Time Series Econometrics, EC542 An Introduction to Modern Econometrics using Stata


65% threshold in each of EC580 Introduction to Econometrics and EC581 Introduction to Time Series Econometrics



Empirical research in macroeconomics as well as in financial economics is largely based on time series, ie chronological sequences of observations, showing the development of quantities, goods and asset prices, and interest rates. The module offers an introduction to contemporary time-series econometrics, linking the theory to empirical studies of the macroeconomy. Topics include: stationary and non-stationary stochastic processes; linear autoregressive and moving average models; linear difference equations; autoregressive distributed lag models; cointegration and equilibrium correction; vector autoregressive models. These topics are illustrated with a range of theoretical and applied exercises, which are discussed in seminars and computer classes.

The module introduces you to the research methods used by macroeconomists in academia, government departments, think tanks and financial institutions. It also helps you to prepare for the quantitative requirements of a masters programme in economics.


This module appears in:

Contact hours

14 lectures (90 minutes)
5 seminar classes (90 minutes)
4 computer practical classes (90 minutes)

Method of assessment

10% In Course Test
20% Group Project
70% Examination (2 hours)

Preliminary reading

While there is no single text covering the whole module, the following books on time series are recommended for reading and reference. Please bear in mind that the main audience of the textbooks are postgraduate students:
• W Enders, Applied Economics Time Series, Wiley, 4th edition (2014) Chapters 1–2, 4–6.2.
Useful guide to empirical modelling.
• PH Franses, D van Dijk and A Opschoor, Time Series Models for Business and Economic Forecasting, Cambridge University Press, 2nd edition (2014). Chapters 2–4, 9.
• JD Hamilton, Time Series Analysis, Princeton University Press (1994). Chapters 1–5, 7–8, 10–11, 15. Handbook-style reference to time series models and econometric methods

See the library reading list for this module (Canterbury)

See the library reading list for this module (Medway)

Learning outcomes

On completion of the module, you will:
• understand the importance of the time series properties of economic data, and be able to test for unit roots;
• understand the analysis of dynamic econometric models including autoregressive distributed lag models;
• be able to formulate, estimate and interpret bivariate relationships between cointegrated series;
• appreciate the importance of the role of stationarity for the properties of classical least squares estimation;
• be able to specify, estimate and critically analyse vector autoregressive and vector equilibrium correction
• models;
• understand the notion of causality and its limitations;
• be able to read, appraise and understand modern journal articles in applied time series econometrics;
• be able to model economic relationships and solve econometric problems with software;
• be able to communicate econometric concepts and findings to your peers.

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