Module Overview

Introduction to Econometrics: 

The history of econometrics, the theory of and aims of econometrics, data types, the population mean and its properties.

                             

Hypothesis Testing:

Hypothesis specification, null and alternative hypothesis, the decision rule, the T-test, type I and type II errors, confidence intervals, the F-test.  

 

Regression Analysis:

Simple and multiple regressions, properties of the error term, regression estimates, regression methods, the residual and fitted values, OLS, R² and adjusted R², reverse regressions, the classical model.   

 

Model Specification and the Associated Problems: 

Choosing the independent variables, omitted variables, irrelevant variables, lagged variables, the Ramsey error specification test, functional form, dummy variables, other specification issues.

 

Multicollinearity: 

Perfect and imperfect multicollinearity, dominant variables, consequences of multicollinearity, detection of multicollinearity and variance inflation factors, remedies for multicollinearity.

 

Serial Correlation:

Pure and impure serial correlation, first-order and higher order serial correlation, consequences of serial correlation, detection of serial correlation and the Durbin-Watson test, correcting serial correlation.

 

Heteroskedasticity:

Pure and impure heteroskedasticity, proportionality factors, consequences of heteroskedasticity, testing for heteroskedasticity using the park test or white test, remedies for heteroskedasticity.

 

Volatility Modelling Using ARCH/GARCH Models: 
The ARCH and GARCH Family of models, testing for ARCH and GARCH effects, estimation issues, multivariate GARCH.

 

Discrete Choice Models:

Models for binary choice, Logit models for multiple choice, models for ordered data.

 

 

Applying Econometric Techniques:

Using statistical software (Stata) to produce econometric output, working with panel data, cross-sectional data and time-series data, testing models.