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>.

Exercises:

· 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.

Links:

	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.