Coronavirus (Covid-19): Latest updates and information
Skip to main content Skip to navigation

EC9C8: Topics in Advanced Econometrics

  • Eric Renault

    Module Leader
  • Mingli Chen

    Module Lecturer
12 CATS - Department of Economics
Spring Module

Principal Aims

EC9C8-12 Topics in Advanced Econometrics

Principal Learning Outcomes

Demonstrate advanced use of R to immediately internalize and use the appropriate techniques in their own academic research. 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: Written assessment 1 (25%)" Demonstrate a deep understanding of recent theoretical developments in the Generalized Method of Moments (GMM), both for cross-section and time series data. 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: Written assessment 2 (25%)" Apply advanced critical thinking skills in the evaluation, selection and application of modern econometric techniques in their own research. 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: Written assessments and presentations. " Demonstrate high level presentation skills. 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: Assessed presentations (2x25%)"

Syllabus

"Part I

The first part of the course will cover Machine Learning in Econometrics. The package R will be used throughout to demonstrate the techniques. The course will provide a practical introduction to modern high-dimensional function fitting methods — a.k.a. machine learning (ML) methods — for efficient estimation and inference on the treatment effects and structural parameters in empirical economic models. Participants will use R to immediately internalize and use the techniques in their own academic and industry work. All lectures, except the introductory ones, will be accompanied by the R-code that can be used to reproduce the empirical examples in the lectures during the lectures. Thus, there will be no gap between theory and practice.

Outline: Review of classical regression for prediction and causal inference; Causal inference in approximately sparse linear structural equations models; Understanding of the inference strategy via the double partialling out and adaptivity; ML methods for prediction (reduced form estimation and evaluation of ML methods using test samples); ML methods for causal parameters, double ML for causal parameters in treat effect models and non-linear econometric models.

Part II

Will review some recent theoretical developments in the Generalized Method of Moments (GMM), both for cross-section and time series data. The focus is on recent advances about empirical likelihood, conditional moment restrictions and indirect inference. By contrast, weak identification, partial identification (moment inequalities) as well as specific approaches for panel data are beyond the scope of this course. The pre-requisite is a working knowledge of conditional expectations, modes of stochastic convergence, maximum likelihood and regression theory (see e.g. Bierens’ textbook).

Outline: Maximum Likelihood, GMM and Empirical Likelihood; Conditional Moment Restrictions; Indirect Inference

"

Context

Optional Module
L1PJ - Year 2
Pre or Co-requisites
Satisfactory completion of MRes year 1

Assessment

Assessment Method
Coursework (100%)
Coursework Details
Coursework (25%), Coursework (25%), Oral Presentation (25%), Oral Presentation (25%)
Exam Timing
N/A

Reading Lists