Introductory description

The module provides students with a thorough understanding of material needed for empirical quantitative analysis, particularly applied econometrics. You will understand how to produce high quality empirical econometric analysis using cross-sectional and panel data, and also learn to interpret critically empirical results.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The syllabus for this module will be based on the following topics; however this list is not limited to those listed below and does not infer all of these topics will be studied in the module:

Introductory Mathematics and Statistics: pre-sessional topics covered will include linear algebra, multivariate calculus and constrained optimisation, differential and difference equations, basic probability theory and hypothesis testing.

The module will emphasise microeconometric applications, and will cover: properties of estimators and how to generate different estimators (Maximum Likelihood Estimation, least squares, method of moments); discrete choice models (binary, unordered multinomial); censored and truncated dependent variable models (Tobit, endogenous selection - Heckman, switching regression models); Linear panel data models; Treatment evaluation methods.

Learning outcomes

By the end of the module, students should be able to:

• Subject-specific skills/Professional Skills:...produce empirical econometric analysis. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination.
• Cognitive skills:...interpret critically empirical results, including the vast array of diagnostic and test statistics often reported, and to come to a balanced view concerning the weight of the empirical evidence presented. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination.
• Subject knowledge and understanding:...demonstrate an understanding of fundamental concepts in mathematics and statistics relevant to the other core modules and be able to apply these concepts to economics. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: test and final examination.
• Subject knowledge and understanding:...demonstrate knowledge and understanding of material needed for empirical quantitative analysis. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination.
• Subject knowledge and understanding:...understand the theory and practice of modern econometrics at a level appropriate for postgraduates emphasising applied econometrics. The teaching and learning methods that enable students to achieve this learning outcome are: lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: final examination.

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Subject specific skills

Students will have the opportunity to develop skills in:
Analytical thinking and communication
Analytical reasoning
Critical thinking
Problem-solving
Abstraction
Policy evaluation
Concepts of Simultaneity and Endogeneity
Analysis of optimisation
Understanding of Uncertainty and Incomplete Information

Transferable skills

Students will have the opportunity to develop:
Numeracy and quantitative skills
Data-based skills
IT skills
Written communication skills
Oral communication skills
Mathematical, statistical and data-based research skills