This information is for the 2017/18 session.
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. This course is available on the MRes/PhD in Economics, MSc in Applicable Mathematics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social 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 for MSc EME (EC451).
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.
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 (50%, duration: 2 hours) in the LT week 0.
Exam (50%, duration: 2 hours) in the main exam period.
Total students 2016/17: 24
Average class size 2016/17: 11
Controlled access 2016/17: Yes
Lecture capture used 2016/17: Yes (LT)
Value: One Unit