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. Introducti
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