Time Series Analysis – Module I


Lecture notes and slides:


·         Syllabus.

·         Introduction to time series.

·         Descriptive analysis of a time series.

·         Time series and stochastic processes.

·         Autoregressive, MA and ARMA processes.

·         Integrated and long memory processes.

·         Seasonal ARIMA processes.

·         Forecasting with ARIMA models.

·         Identifying possible ARIMA models.

·         Estimation and selection of ARIMA models.

·         Model diagnosis and prediction.


Data files:


·         <EViewsDatafiles.zip>.




·         TSA exercises.


Examples of TSA projects:


·         Modelling time series of carbon dioxide emissions in Rome city.

·         Detrending the business cycle: Hodrick-Prescott and Baxter-King filters.

·         Demand/suply: A linear relation over time? Insights from Australia.

·         Caracterization of the Argentine business cycle.

·         Global warming or global warning? The problems of testing for a trend in enviromental time series.




Statistical Methods for Business and Economics.

Faculty of Economics-Skopje (ECCF).

Department of Economics, Università degli studi Roma Tre.

Department of Statistics, Universidad Carlos III of Madrid.