EC9A3: Advanced Econometric Theory
Kenichi Nagasawa
Module Leader
Introduction
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 Aims
The module aims to develop the 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.
Subject Knowledge and Understanding: demonstrate advanced understanding of material required for empirical quantitative analysis.
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.
Subject Knowledge and Understanding:...demonstrate advanced knowledge of recent research in the key areas of econometric theory.
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); hypothesis testing (trinity of asymptotic tests), Extremum estimators (consistency, asymptotic distribution); application to MLE, M-Estimators, IV and GMM. Linear and non-linear statistic and dynamic panel data models; treatment effect estimation under randomized control trials, treatment effect estimation under selection-on-observables, treatment effect estimation with instrumental variables, differences-in-difference methods; Univariate ARMA, VAR (Vector Autoregressive), Causality, GARCH, Stochastic Volatility, State Space Models, Indirect Inference鈥�.
Context
- Core Module
- L1PL - Year 1
- Optional Module
- N3P5 - Year 2
Assessment
- Assessment Method
- Coursework (100%)
- Coursework Details
- In class test 1 (20%) , In class test 2 (20%) , In department exam (60%)
- Exam Timing
- N/A