Automatic Model Selection Methods
The purpose of this course is to provide students with an introduction to automatic model selection and its limitations and uses in practical econometric modelling. In addition, the course introduces saturation estimation techniques and considers other approaches.
Prerequisites
Please note to take this course you must first have completed Foundations of Econometrics.
Delivery
Two-hour lectures per day over five days, plus a one-hour tutorial each day, with optional clinics on the final day. Sessions are live online and recorded.
Study Hours
Approximately 100 hours of study, of which 15 are contact hours and 85 are private study.
Software Used
OxMetrics 9
Credits
10 CATS credits
Programme Faculty
Key Outcomes
- —Theoretical properties of automatic model selection
- —Using and understanding results from the Autometrics algorithm in PcGive
- —Have a basic understanding of the Autometrics algorithm
- —Saturation estimation, such as indicator, step, and trend indicator saturation
- —Alternative Lasso based methods
Desired Skills
- —Use automatic model selection to build econometric models
- —Use saturation estimation
- —Present, interpret and analyse the results of automatic model selection
- —Utilise effectively PcGive and the R gets package
Lecture Programme
- WK 01
Lecture 1: Theory
- ·Theory of reduction and model evaluation
- WK 02
Lecture 2: Practice and algorithms
- ·Introduction to automatic model selection; understanding Autometrics
- WK 03
Lecture 3: Practice of automatic model selection
- ·Empirical modelling with automatic model selection in action
- WK 04
Lecture 4: Saturation methods
- ·Impulse indicator and other saturation methods
- WK 05
Lecture 5: Extensions and different approaches
- ·Further uses of automatic model selection and alternative methods
Assessment
Online coursework submission with the support of your module lecturer and tutor. All sessions are conducted live online at a scheduled time and recorded for review.