Syllabus for Time Series Analysis

Learning outcomes

A student that has completed the course should

? have deeper knowledge of statistical theory and methods particularly common problems in economical social sciences especially economics.

? be able to estimate models for time-series data.

? be able to interpret the results of an implemented statistical analysis

? be aware of limitations and possible sources of errors in the analysis

? have ability to present results in oral and written form

Content

Overview of forecasting. Models for time series: Time-dependent seasonal components. Autoregressiva (AR), moving average (MA) and mixed ARMA-modeller. The Random Walk Model. Box-Jenkins methodology. Forecasts with ARIMA and VAR models.

Dynamic models with time-shifted explanatory variables. The Koyck transformation . ?Partial adjustment? and ?adaptive expectation? models. Granger's causality tests. Stationarity, unit roots and cointegration. Modelling of volatility: ARCH - and the GARCH-models.

Instruction

Lectures

Assessment

The examination comprises a written test at the end of the course and compulsory assignments, (laboratory sessions). Three grades are awarded for the course: not passed, passed, and passed with distinction.