Time Series Analysis

7.5 credits

Syllabus, Bachelor's level, 2ST093

Code
2ST093
Education cycle
First cycle
Main field(s) of study and in-depth level
Statistics G1F
Grading system
Fail (U), Pass (G), Pass with distinction (VG)
Finalised by
The Faculty Board of Social Sciences, 31 May 2007
Responsible department
Department of Statistics

Entry requirements

Statistics, 30 credits points or equivalent

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 6-10 hours a week.

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.

FOLLOW UPPSALA UNIVERSITY ON

facebook
instagram
twitter
youtube
linkedin