EC338: Econometrics 2: Microeconometrics
Manuel Bagues
Module Leader
Introduction
EC338-15 Econometrics 2: MicroeconometricsPrincipal Aims
The aim of the module is to provide students with the theoretical and empirical tools to conduct rigorous applied microeconomics research. The course builds on the structural empirical methods learnt in the second year, expanding knowledge of limited dependent variable methods and panel data models. It also provides an introduction to programme evaluation techniques, exploring ways in which researchers can identify causal relationships. This module will be particularly valuable to students interested in conducting applied economics research as part of their final year dissertation, or in their future careers.
Principal Learning Outcomes
Subject knowledge and understanding:...understand what is meant by a 鈥渃ausal鈥� estimate and what is required to identify such a relationship.
Subject knowledge and understanding:...understand the theoretical pros and cons – and the empirical requirements – of a range of methods designed to produce causal estimates.
Subject knowledge and understanding:...be able to analyse different types of microeconomic data using appropriate empirical techniques.
Subject knowledge and understanding:...be able to interpret the results of such analyses.
Subject knowledge and understanding:...understand the features of a robust evaluation and be able to design one for a given scenario.
Subject-specific and Professional Key General Skills:...demonstrate an ability to critically assess empirical papers.
Subject-specific and Professional Key General Skills:...be equipped to tackle advanced microeconometric modules at postgraduate level.
Syllabus
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.
Context
- Optional Module
- GL11 - Year 3, GL12 - Year 4, L100 - Year 3, L103 - Year 4, L116 - Year 3, L1P5 - Year 1, L1PA - Year 1, LM1D (LLD2) - Year 3, LM1H - Year 4, V7ML - Year 3, V7MM - Year 4, V7MP - Year 3, V7MR - Year 3, LA99 - Year 3, R9L1 - Year 4, R3L4 - Year 4, R4L1 - Year 4, R2L4 - Year 4, R1L4 - Year 4, L1L8 - Year 3, L1CA - Year 3, L1CB - Year 4
- Pre or Co-requisites
EC203-30 OR
EC226-30 OR
ST218-12 and ST219-12
Summary:Modules: EC203-30 or EC226-30 or (ST218-12 and ST219-12)
Assessment
- Assessment Method
- Coursework (40%) + Centrally-timetabled examination (On-campus) (60%)
- Coursework Details
- Assignment 1 (1500 words) (20%) , Assignment 2 (1500 words) (20%) , Centrally-timetabled examination (On-campus) (60%)
- Exam Timing
- Summer