Learning outcomes:

On successful completion of this module students will be able to:

  • Define and explain concepts such as stationarity and non-stationarity
  • Developing forecasting models for time series with the use of an econometric software package (e.g. E-views).
  • Critically evaluate and assess time series models and their results
  • Critically evaluate and assess the results of diagnostic tests.

Use models to make forecasts on time series.


  • Code :             X81
  • Semester :     8th
  • Effort :              7.5 ECTS
  • Hours :             2 per week + 1 Laboratory
  • Type :                Mathematics
  • Department : Economics
  • Exams :             Yes

Course Contents:

  • The module focuses on the analysis of time series which is one of the important types of data that are being used in the empirical analysis. The module aims to familiarize students with the necessary statistical concepts and the use of appropriate econometric techniques for the development of time series forecasting models with the use of an econometric software package (e.g. E-views).Suggested Module Content:
    • Introduction to time series
    • Stochastic processes and basic concepts
    • Autoregressive (AR) models
    • Moving Average (MA) models
    • Autoregressive and Moving Average (ARMA) models
    • Autoregressive Integrated Moving Average (ARIMA) models
    • Diagnostic tests and model selection criteria
    • Forecasting
    • Volatility models (ARCH-GARCH)