EC9C8-12 Topics in Advanced Econometrics
Introductory description
EC9C8-12 Topics in Advanced Econometrics
Module aims
The module aims to develop the skills and knowledge of advanced econometrics necessary for a career as an academic economist and in all areas where advanced research skills in econometrics are required. Specifically, it aims to teach the students to understand, appreciate, and ultimately contribute to, frontier research.
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.
"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
Part II
Will review some recent developments in unsupervised learning and causal machine learning with panel data. The focus is on recent advances about factor model, clustering, and text analysis.
Outline: Basics of unsupervised learning, causal inference/learning with panel data
"
Learning outcomes
By the end of the module, students should be able to:
- 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 (50%)
- 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 (50%)
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
Understanding of Uncertainty and Incomplete Information
Transferable skills
Students will have the opportunity to develop:
Research skills
Numeracy and Quantitative skills
Written communication
Oral communication
Mathematical, Statistical, data-based research skills
Study time
| Type | Required |
|---|---|
| Lectures | 30 sessions of 1 hour (25%) |
| Private study | 90 hours (75%) |
| Total | 120 hours |
Private study description
Private study will be required in order to prepare for seminars/classes, to review lecture notes, to prepare for forthcoming assessments, tests, and exams, and to undertake wider reading around the subject.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A5
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Research Report | 50% | No | |
Reassessment component is the same |
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Assessment component |
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| Presentation | 50% | No | |
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Presentation of Referee Report |
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Reassessment component is the same |
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Feedback on assessment
The Department of Economics is committed to providing high quality and timely feedback to students on their assessed work, to enable them to review and continuously improve their work. We are dedicated to ensuring feedback is returned to students within 20 University working days of their assessment deadline. Feedback for assignments is returned either on a standardised assessment feedback cover sheet which gives information both by tick boxes and by free comments or via free text comments on Tabula, together with the annotated assignment. For tests and problem sets, students receive solutions as an important form of feedback and their marked assignment, with a breakdown of marks and comments by question and sub-question. Students are informed how to access their feedback, either by collecting from the Department of Economics Postgraduate Office or via Tabula. Module leaders often provide generic feedback for the cohort outlining what was done well, less well, and what was expected on the assignment and any other common themes. This feedback also includes a cumulative distribution function with summary statistics so students can review their performance in relation to the cohort. This feedback is in addition to the individual-specific feedback on assessment performance.
Pre-requisites
Satisfactory completion of MRes year 1
Courses
This module is Core optional for:
- Year 2 of TECA-L1PL in Economics (Master of Research plus PhD)