The University of Southampton
Courses

# ECON6024 Econometrics 1

## Module Overview

To provide you with a knowledge and understanding of the statistical foundations of econometric theory. In addition to the “classical” likelihood theory, emphasis will be placed on recent developments in the statistical theory of asymptotic approximations, empirical likelihood, and problems of identification. Some of the material may familiar to students with strong backgrounds in statistical theory, but the module seeks to develop a deeper understanding of the material.

### Aims and Objectives

#### Module Aims

The aim of the module is to provide you a knowledge and understanding of the statistical foundation s of modern econometric theory. At the end of the module you should be able to appreciate econometric work (both theory and applied) that is currently appearing in the top Economics and Econometrics Journals (Econometrics, JPE, AER, Journal of Econometrics, Econometric Theory, etc). You will also hopefully, have ideas about areas of econometrics that still pose unanswered questions, and be keen to answer them!

#### Learning Outcomes

##### Learning Outcomes

Having successfully completed this module you will be able to:

• Assimilate frontier econometric research, as published in leading journals
• Identify new and interesting topics for future research
• Tackle such advanced problems for yourself

### Syllabus

The main topics to be covered are: • Multiple Regression; Testing; Diagnostics (Mainly Revision) • Statistical Models, Likelihood, Sufficiency • Exponential Family • Asymptotic Theory, Saddlepoint Approximations • Theory of Hypothesis Testing, Neyman-Pearson Lemma • Empirical Likelihood • Multivariate Linear Models (SUR, SEM) • Identification

#### Special Features

High-Level econometric Theory based firmly in Statistical Theory

### Learning and Teaching

TypeHours
Independent Study160
Teaching40
Total study time200

Statistical Foundations for Econometric Techniques.

Asymptotic Techniques for use in Statistics.

Inference and Asymptotics.

Other course information available via blackboard.

Theoretical Statistics.

Elements of Modern Asymptotic theory with Statistical Applications.

### Assessment

#### Assessment Strategy

Assessment is by three hour written examination at the end of Semester 1. Summative Referral: 100% Examination

#### Summative

MethodPercentage contribution
Exam  (3 hours) 100%

#### Referral

MethodPercentage contribution
Exam 100%

#### Repeat Information

Repeat type: Internal & External