Final year module
Lecturer: Marco R Barassi
Weeks 1 to 6 cover: Maximum Likelihood Estimation in general; properties of the score, information, efficiency, consistency, asymptotic distributions. Tests based on MLE: Likelihood ratio, Wald and Lagrange Multiplier. Weeks 7 to 11 introduce stationary and non-stationary time series, unit root testing and cointegration. GARCH Models.
On completion of this module the student will be able to: construct the likelihood function for simple models, derive the score and information, MLE, and develop their properties; derive simple properties of some elementary time series models, and demonstrate important difference between stationary and non-stationary processes.