Postgraduate Course: Advanced Microeconometrics (ECNM11048)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces students to the econometric analysis of duration data (weeks 1, 2 and 3) and regression with Big Data using Machine Learning techniques (weeks 4, 5 and 6). The course maintains a dual focus on theory and application and considers the use of econometric models for descriptive analysis, prediction and for the estimation of causal effects. |
| Course description |
The course is organised as weekly three-hour lectures. Tentative lecture plan as follows:
1. Econometric models of duration and transition data
2. Identification of duration models
3. Treatment effects in duration models
4. Basic concepts, predictive and causal inference
5. Regularization methods
6. Double Machine Learning techniques
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Econometrics 1 (ECNM11043) AND
Econometrics 2 - Microeconometrics (ECNM11091)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students should be enrolled on MSc Economics, MSc Economics (Econometrics), MSc Economics (Finance) or MSc Mathematical Economics and Econometrics.
Any other students must email sgpe@ed.ac.uk in advance to request permission.
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
78 )
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
2-hour final exam (100%) |
| Feedback |
Feedback will be provided in accordance with the feedback policy for SGPE/PGT courses. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- 1. Opportunity to develop and demonstrate knowledge and understanding of the econometric analysis of duration data.
- 2. Opportunity to develop and demonstrate knowledge and understanding of regression with Big Data using Machine Learning techniques.
- 3. Opportunity to develop and demonstrate investigative skills such as problem solving and the ability to assemble and evaluate complex evidence and arguments.
- 4. Opportunity to develop and demonstrate communication skills in order to critique, create and communicate understanding.
- 5. Opportunity to develop and demonstrate personal effectiveness through task- and time-management, dealing with uncertainty and adapting to new situations, personal and intellectual autonomy through independent learning.
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Reading List
¿ Cameron and Trivedi, ¿Microeconometrics: Methods and Applications,¿ Cambridge University Press
¿ Chernozhukov et al. (2024), ¿Causal ML Book: Applied Causal Inference Powered by ML and AI¿, https://causalml-book.org
¿ Chernozhukov et al. (2018), ¿Double/debiased machine learning for treatment and structural parameters,¿ The Econometrics Journal 21(1), 1-68.
¿ James et al. (2021), ¿An Introduction to Statistical Learning with Applications in R,¿ Springer
¿ Ridder and van den Berg (2003), ¿The Nonparametric Identification of Treatment Effects in Duration Models,¿ Econometrica 71 (5), 1491-1517
¿ van den Berg (2001), ¿Duration Models: Specification, Identification, and Multiple Durations,¿ Handbook of Econometrics, ch. 55
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Prof Jesper Bagger
Tel:
Email: jbagger@exseed.ed.ac.uk |
Course secretary | |
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