Time Series Econometrics and Forecasting
The purpose of thiscorecourse is to provide studentswith an in-depthunderstanding of the fundamental concepts oftime series econometrics andforecasting and with the practicalskills to use econometric software to model and forecast economic time seriesand identify models with thebest forecasting abilities.The module would build on the foundation of econometrics core course and preparestudents for MScand PhDresearch.
This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program.
This module will begin on 26/10/2021.
Final date to apply: 16/10/2021.
By the end of this course, participants should have knowledge and ability to:
Understand how to model a univariate or multivariate time series process.
Understand the Maximum Likelihood estimation method, and understand how to forecast a univariate or a multivariate time series process
Distinguish between stationary and nonstationary series and understand the implications of using nonstationary series.
Build, estimate and forecast from univariate and multivariate time series models using econometric software.
By the end of this course, students should be able to:
Engage in abstract thinking by extracting the essential features of complex systems to facilitate problem solving and decision-making.
Communicate and present complex arguments in oral and written form with clarity and succinctness.
Use statistical and other packages effectively, presenting, interpreting and analysing information in numerical form.
Apply econometrics techniques to aid understanding of the financial and macroeconomic environment.
Time Series Econometrics and Forecasting is a core 20 credit course and therefore students are expected to input approximately 200 hours of study into the course.
The total number of contact hours is 30 hours. This leaves 170 hours for private study.
This module consists of 2- hour lectures per day, for 5 consecutive days, plus a 1 - hour tutorial per day.
There will be optional clinics on the last day of the course.