Quantitative Techniques for Financial Economics III

EMEC022S6 (30 credits)
Autumn Term

Lecturer: Zacharias Psaradakis

Aims

This course aims to provide an introduction to the fundamental theoretical concepts and applications of econometrics. The course gives students an understanding of the science and art of determining what type of model to build, estimating the parameters of the model, testing the model statistically, and applying the model to practical problems in forecasting and policy analysis. Students will also learn how to do empirical econometrics using the EViews software package.

Objectives

At the end of the course, students will be able to demonstrate that they can:

• understand the assumptions and uses of the multiple linear regression model;
• derive the OLS estimator and establish its properties;
• understand the basic principles of hypothesis testing and conduct significance tests in linear regression models;
• derive the GLS estimator for models with heteroscedastic or autocorrelated errors and understand its properties;
• explain how to carry out tests for heteroscedasticity, autocorrelation, and parameter non constancy;
• explain the basic principles of instrumental variables estimation;
• use standard econometrics packages for regression analysis and interpret their output.

Assessment

The three-hour June exam counts for 80% and an assignment over the Christmas vacation counts for the remaining 20%.

Principle Texts

• Gujarati, D. N., and Porter, D. C., Basic Econometrics, (5th edition), New York: McGraw-Hill, 2009.
• Johnston, J. and DiNardo, J., Econometric Methods, (4th edition), New York: McGraw-Hill, 1997.

Outline of Topics

1. Review of the two-variable linear regression model.
2. Specification and estimation of multiple regression models.
3. Hypothesis testing in multiple regression models.
4. Specification errors, multicollinearity and structural change.
5. Heteroscedasticity.
6. Autocorrelation.
7. Stochastic regressors and instrumental variables.
8. Empirical applications.