Motivation:

A time series is a collection of observations made sequentially in time. Examples

of time series are the daily closing value of the Dow Jones index, monthly returns from stock markets, daily mortality counts, air pollution measurements, temperature data, traffic accidents, etc. The difference of time series from other modelling methods is that time series analysis accounts for the fact that data points taken over time may have an internal structure.

Goal:

The goal of the course is to introduce different statistical models for time series and learn the main methods to estimate these models. The students will be able to learn how to identify the ARIMA (Autoregressive Integrated Moving Average) models by analyzing the autocorrelation and partial- autocorrelation graphs, fit the models and forecast the time series for the given financial time series using the available software packages.

Course Outline (Subject to change):

1.     Introduction, difference equations and linear models

2.     Unit-root, ARIMA models, seasonal models, and aggregation

3.      Forecasting and model building

4.     Model specification and Estimation

5.     Introduction to Unit Roots and testing methods.

Software used:

·       R – package;

İnstructor – Bahar Dadashova

Bachelor deegre – Azerbaijan Economics University "İnternational Economics relations”

 – "Group of Special Talantes”

 Master deegre - Madridin Carlos III University "Mathematics profession of Engineering”

Studies PhD -  Madrid Politexnik Univniversity PhD "Mechanical Profession of  Engineer”

Works: INSIA (INISTITUTE FOR AUTOMOBILE RESEARCH).

Bibliography (Subject to Change) :

·       Hamilton, J. D. (1994) Time Series Analysis;

·       Box, G., Jenkins, G. M. And Reinsel, G. (1994) Time Series Analysis: Forecasting and Control;

·       Peña, D., Tiao, G. C. And Tsay, R. S. (2001) A Course in Time Series;

·       Tsay, R. S. (2005) Analysis of Financial Time Series.

OYU Adminstration