You are here Undergraduate

Econometrics A

Module Code: ECU33091

Module Title: Econometrics A

• ECTS Weighting: 5
• Semester/Term Taught: Semester 1
• Contact Hours: 22 hours of lectures, 5 hours of tutorials and 5 hours of computer workshops
• Module Personnel: Lecturer - Barra Roantree

*Please note that assessment details have not been finalised yet and are subject to change

Learning Outcomes

On successful completion of this module, you will be able to:

• Confidently discuss core underlying statistical principals;
• Derive estimators for linear regression and show their properties;
• Derive appropriate tests for the underlying assumptions of the general linear model and demonstrate how to correct for violations of these assumptions;
• Confidently discuss the problem of identification;
• Use the techniques developed to test simple economic models;
• Use STATA to estimate econometric models.

Module Learning Aims

This module provides an introduction to the theory and methods of modern econometrics. It begins by reviewing and extending the statistical material covered in the senior freshman year. Following this students are guided through the fundamental principles of econometrics and working through to more advanced topics as the module progresses. The module provides a balance between core theoretical material and an extensive applied component which aims to develop student's practical skills necessary to conduct independent applied research.

Module Content

Topics discussed during Michaelmas Term include:

• Statistical Review: Populations, parameters and random sampling; finite-sample properties of estimators, introduction to asymptotic theory; methods of estimation (method of moments, maximum likelihood, least squares, interval estimation); hypothesis testing.
• The Simple Regression Model: Ordinary Least Squares (OLS) estimation; properties of OLS; Goodness of Fit; Functional Form.
• Multiple Regression Analysis: Estimation and interpretation; model specification (determining which variables to include and which functional form to use); the multicollinearity problem.
• Inference: hypothesis testing in the context of multiple regression analysis using t-tests and F-tests.
• Dummy Variables: Binary and categorical explanatory variables in regression analysis.

Recommended Reading List

Primary Texts:

• J.M. Wooldridge, Introductory Econometrics: A Modern Approach, 5/e, Cengage, 2013
• D. Gujarati and D. Porter, Basic Econometrics, 5/e, McGraw-Hill, 2011
• Enders Walter, Applied Econometric Time Series, 4/e, Wiley, 2014

Secondary Texts:

• Angrist, J.D. and Pischke, J. Mastering Metrics, Princeton University Press, 2015
• Gujarati, D., Econometrics by Example, Palgrave MacMillan, 2011

Module Pre Requisite

ECU22031 & ECU22032 Mathematical and Statistical Methods

Module Co- Requisite

ECU33092 Econometrics B

Assessment Details

• 4 homework assignments accounting for 20% of the overall grade.
• A computer assignment which is worth 10% of the overall grade.
• A final exam which is worth 70% of the overall grade.

Module Website

Blackboard