Applied Macroeconometrics

Module code: ECN602

Applied Macroeconometrics aims to:

  • enable you to understand recent applied literature in core journals of macroeconomics and finance which uses time series methods
  • prepare you for possible later research involving time series.

This is a core module for:

Learning objectives

The intended learning outcomes are that by the end of the module you should be able to:

  • identify empirical features and characteristics of various types of macroeconomic and financial data
  • develop a firm understanding of the key econometric techniques used to analyse macroeconomic and financial data by scholars and market analysts
  • correctly apply testing and forecasting techniques as dictated by the data and selected model
  • understand how time-varying models may capture the changing properties of macroeconomic variables over business cycle expansions and contractions
  • critically evaluate empirical studies in macroeconomics and finance and appreciate some of the problems associated with estimating time series data
  • extensively use STATA econonometric software to analyse macroeconomic and financial data


The syllabus will aim to include the following topics:

  1. Fundamental concepts: white noise processes, stationarity, autocovariance and autocorrelation functions.
  2. Stationary time series: autoregressive models (AR), moving average models (MA), autoregressive moving average models (ARMA).
  3. Model building: identification, estimation, diagnostic checking, model selection criteria.
  4. Non-stationary time series: trends, unit roots, testing for unit roots, structural change.
  5. Time series models of heteroscedasticity: autoregressive and generalized autoregressive conditional heteroscedasticity models (ARCH and GARCH).
  6. Multivariate time series models: cointegration, error correction mechanisms, vector autoregressive models (VAR) - estimation, identification and causality.
  7. Multivariate time series models: cointegration in a VAR, vector error correction models (VEC), illustration: money demand, Fisher relation and risk premium.
  8. Forecasting: properties of optimal forecasts, computation of forecasts, updating forecasts.

Teaching methods

There will be ten two-hour lectures and four one-hour computer labs.


The assessment of this module will be by a group assignment (25%) and an unseen exam (75%).

Basic reading

We advise you not to buy books before the module begins, as the reading list may change. If you wish to read in advance, look for these texts in the University library

The following textbooks can be used as background reading:

Enders, W. (2009). Applied Econometric Time Series. 3rd Edition. John Wiley and Sons Inc.

Verbeek, M. (2012). A guide to modern econometrics. 4th Edition. John Wiley and Sons Inc.

Becketti, S. (2013). Introduction to time series using Stata. 1st Edition. Stata Press.

Prerequisites ECN6540 Econometric Methods
This module requires a strong background in econometrics

Module leader Dr Georgios Efthyvoulou

Please note that the leader may change before the module begins

Semester Spring

Credits 15