EC9A3: Advanced Econometric Theory
Eric Renault
Module Leader
Kenichi Nagasawa
Module Lecturer
Principal Aims
The module provides students with skills and knowledge of econometrics necessary for a career as an academic economist and in all areas where advanced research skills in economics are required. Specifically, the students will learn to understand, appreciate, and ultimately contribute to, frontier research. It is intended to be comparable to modules taught in the best research universities in the USA and elsewhere in Europe.
Principal Learning Outcomes
Subject Knowledge and Understanding:...demonstrate an advanced understanding of the main aspects of modern econometric theory and techniques used in research at the forefront of the field. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and independent study. The summative assessment methods that measure the achievement of this learning outcome are: Assessments.
Subject Knowledge and Understanding: demonstrate advanced understanding of material required for empirical quantitative analysis. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and independent study. The summative assessment methods that measure the achievement of this learning outcome are: Assessments.
Cognitive Skills: be in a position to critically select, evaluate and apply modern econometric techniques in their own research both in terms of theoretical as well as empirical work. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and independent study. The summative assessment methods that measure the achievement of this learning outcome are: Assessments.
Syllabus
Illustrative topics might include: Review of Probability theory; Large sample inference to include modes of convergence, LLN, CLT, and the Delta method; Linear regression (consistency and asymptotic distribution); hypotheses testing (trinity of asymptotic tests), Extremum estimators (consistency, asymptotic distribution); application to MLE , M-Estimators, IV and GMM. Linear and non-linear static and dynamic panel data models including the case of endogenous regressors, Causal Identification.
Context
- Core Module
- L1PL - Year 1
- Optional Module
- N3P5 - Year 2
Assessment
- Assessment Method
- Coursework (100%)
- Coursework Details
- Test 1 (25%) , Test 2 (50%) , Test 3 (25%)
- Exam Timing
- N/A
Exam Rubric
Time Allowed: 3 Hours
Read all instructions carefully - and read through the entire paper at least once before you start entering your answers.
Answer ALL questions.
Answer each whole question in a separate booklet.
Approved scientific (non-graphical) pocket calculators are allowed.
Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.