Econometric Analysis

This information is for the 2019/20 session.

Teacher responsible

Prof Javier Hidalgo 32L.4.20 and Prof Taisuke Otsu 32L.4.25


This course is compulsory on the MSc in Econometrics and Mathematical Economics. This course is available on the MRes/PhD in Economics and MSc in Applicable Mathematics. 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).

EC451 takes place prior to the start of Michaelmas Term, please contact for more information.

Course content

This course gives an advanced treatment of the theory of estimation and inference for econometric models.

Part (a) Background; asymptotic statistical theory: modes of convergence, asymptotic unbiasedness, uniform integrability, stochastic orders of magnitude, convergence in distribution, central limit theorems, applications to linear regression, extensions to time series, consistency and asymptotic distribution  of implicitly defined extremum  estimators.

Part (b) General asymptotic theorems, nonlinear regression, quantile regression, nonparametric methods (kernel and series methods), generalized method of moments, conditional moment restriction, many and weak instruments, limited dependent variables, treatment effect, bootstrap, and time series.


20 hours of lectures and 10 hours of seminars in the MT. 20 hours of lectures and 10 hours of seminars in the LT.

Formative coursework

Two marked assignments per term.

Indicative reading

No one book covers the entire syllabus; a list of references will be provided at the start of the course, and lecture notes and relevant articles will be circulated.


Exam (50%, duration: 2 hours) in the January exam period.
Exam (50%, duration: 2 hours) in the summer exam period.

Key facts

Department: Economics

Total students 2018/19: 41

Average class size 2018/19: 14

Controlled access 2018/19: No

Value: One Unit

Guidelines for interpreting course guide information