- Degree Master
- Code: IFI714
- Credit hrs: 3
- Prequisites: -
Econometrics is the unification of statistics, economic theory and mathematics. Econometrics is a branch of economics that seeks to measure and correlate the relationships among economic variables. It combines economic theory expressed in mathematical form with statistical methods. It helps in forecasting economic indicators, assist in evaluating effects of policies both before and after implementation, and examine or prove an economic theory. The main objective of econometrics is to identify a causal effect of one or more variables (independent) on another variable (dependent).
Master of Science (MSc)
Lectures are not based strictly on text material. Many subject areas covered in the text will be supplemented with additional material or presented in an alternative manner in class. For this reason, exams will be based on both class lectures and text material.
| content serial | Description |
|---|---|
| 1 | Probability and statistical Models • introduction • Axioms of probability • Independence • Bayes’ law • Probability distribution • Function of random variables • Common discrete distribution • Common continuous distribution Normal probability plot |
| 2 | Returns • Net return • Gross return • Log return • Adjustment for dividends • Random walk model |
| 3 | Visualizing time series data structure • Introduction • Graphical analysis of time series • Graph terminology • Graph perception • Principles of graph construction • Time series plot |
| 4 | Stationary time series models • Basic of stationary time series models • Autoregressive Moving Average (ARMA) models • Stationary and inevitability of ARMA models • Checking for stationary using Variogram • Transformation of data |
| 5 | Nonstationary time series model • Detecting nonstationary • Autoregressive Integrated Moving Average (ARIMA) models • Forecasting using ARIMA models Time series model selection • Finding the “BEST” model • Model selection criteria |
| 1 | Probability and statistical Models • introduction • Axioms of probability • Independence • Bayes’ law • Probability distribution • Function of random variables • Common discrete distribution • Common continuous distribution Normal probability plot |
| 2 | Returns • Net return • Gross return • Log return • Adjustment for dividends • Random walk model |
| 3 | Visualizing time series data structure • Introduction • Graphical analysis of time series • Graph terminology • Graph perception • Principles of graph construction • Time series plot |
| 4 | Stationary time series models • Basic of stationary time series models • Autoregressive Moving Average (ARMA) models • Stationary and inevitability of ARMA models • Checking for stationary using Variogram • Transformation of data |
| 5 | Nonstationary time series model • Detecting nonstationary • Autoregressive Integrated Moving Average (ARIMA) models • Forecasting using ARIMA models Time series model selection • Finding the “BEST” model • Model selection criteria |
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