Introductory description

EC338-15 Econometrics 2: Microeconometrics

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 module will typically cover the following topics: Introduction to correlation vs. causation; OLS and propensity score matching; introduction to dealing with unobservable characteristics; randomised control trials; instrumental variables;regression discontinuity design; difference-in-differences; static and dynamic linear panel data methods; introduction to maximum likelihood estimation; binary choice models; discrete choice models.

Learning outcomes

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

• Subject knowledge and understanding: Understand what is meant by a “causal” estimate and what is required to identify such a relationship The teaching and learning methods that enable students to achieve this learning outcome are: Lectures The summative assessment methods that measure the achievement of this learning outcome are: Examination and coursework assignment
• Subject knowledge and understanding: Understand the theoretical pros and cons – and the empirical requirements – of a range of methods designed to produce causal estimates The teaching and learning methods that enable students to achieve this learning outcome are: Lectures The summative assessment methods that measure the achievement of this learning outcome are: Examination and coursework assignment
• Subject knowledge and understanding: Be able to analyse different types of microeconomic data using appropriate empirical techniques The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and seminars The summative assessment methods that measure the achievement of this learning outcome are: Coursework assignment
• Subject knowledge and understanding: Be able to interpret the results of such analyses The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and background reading The summative assessment methods that measure the achievement of this learning outcome are: Examination and coursework assignment
• Subject knowledge and understanding: Understand the features of a robust evaluation and be able to design one for a given scenario The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and background reading The summative assessment methods that measure the achievement of this learning outcome are: Examination and coursework assignment
• Subject-specific and Professional Key General Skills Demonstrate an ability to critically assess empirical papers The teaching and learning methods that enable students to achieve this learning outcome are: Seminars and background reading The summative assessment methods that measure the achievement of this learning outcome are: Coursework assignment
• Subject-specific and Professional Key General Skills Be equipped to tackle advanced microeconometric modules at postgraduate level The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and background reading The summative assessment methods that measure the achievement of this learning outcome are: Examination and coursework assignment

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
Creative 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:
Research skills
Numeracy and quantitative skills
Data-based skills
IT skills
Written communication skills
Oral communication skills
Mathematical, statistical and data-based research skills