Dr Taisuke Otsu 32L. 4.25 and Professor Peter Robinson 32L. 4.13

This course is compulsory on the MSc in Econometrics and Mathematical Economics and MSc in Econometrics and Mathematical Economics (2 Year Programme). This course is available on the MRes in Economics (Track 1), MRes in Economics (Track 2), MSc in Applicable Mathematics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

Students must have completed Introductory Course in Mathematics and Statistics (EC400).

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

Part (a) Matrix 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, consistency and asymptotic normality of implicitly defined extremum  estimators .

Part (b) Non-linear-in variables systems: maximum likelihood and instrumental variables estimates, optimal instrumental variables estimates for static and dynamic models, and models with autocorrelated disturbances. Simultaneous equations systems, identification, estimation, asymptotic behaviour of estimators and hypothesis testing. Wald, generalised likelihood ratio and Lagrange multiplier hypothesis tests, asymptotic null and local behaviour and consistency.

30 hours of lectures and 15 hours of seminars in the MT. 30 hours of lectures and 15 hours of seminars in the LT.

Two marked assignments per term.

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 (100%, duration: 3 hours) in the main exam period.