Professor Javier Hidalgo SAL 4.20

Dr Yike Wang SAL 4.26

Dr Vassilis Hajivassiliou SAL 4.23

This course is available on the MSc in Econometrics and Mathematical Economics. This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must have completed Introductory Course for MSc EME (EC451).

In exceptional circumstances, students may take this course without EC451 provided they meet the necessary requirements and have received approval from the course conveners (via a face to face meeting), the MSc EME Programme Director, and their own Programme Director. Contact the Department of Economics for more information (econ.msc@lse.ac.uk) regarding entry to this course.

The aim of the course is to introduce the student to topics at the frontier of econometric research of importance both at a theoretical and empirical level. The course consists of four series of ten lectures on specialised topics in econometrics. These lectures change from year to year. For the academic year 2023-2024, they will include: Bootstrap methods; nonparametric and semiparametric methods in econometrics; high dimensionalities and machine learning; and nonlinear dynamic panel data models.

20 hours of lectures in the AT. 20 hours of lectures in the WT.

This course is delivered through lectures totalling a minimum of 40 hours across Autumn Term and Winter Term. There are no classes.

No one book covers the entire syllabus; lists of references will be provided and lecture notes circulated.

Exam (50%, duration: 2 hours, reading time: 15 minutes) in the January exam period.
Exam (25%, duration: 1 hour, reading time: 15 minutes) in the spring exam period.
Essay (25%, 2000 words) in the ST.

• The January exam is based on Prof Hidalgo's lectures on bootstrap methods and on nonparametric and semiparametric methods.

• The Spring exam is based on Dr Hajivassiliou’s lectures on panel data methods

The essay due in Spring Term is based on Dr Wang's teaching on high dimensionalities and machine learning, which provides an opportunity to critically review an academic paper.