Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: ECO302
Module Title: Advanced Econometrics
Module Level: Level 3
Module Credits: 5.00
Academic Year: 2017/18
Semester: SEM1
Originating Department: International Business School
Pre-requisites: N/A
   
Aims
The aim of this module is to give students an understanding of econometric time-series methodology. The module will build upon the materials of Basic Econometrics. Important extensions include volatility models of financial time-series, and multivariate (multiple equation) models such as vector error correction and related cointegrating error correction models.
Learning outcomes 
After successful completion of this model, students will be able to:

-- be able to specify and demonstrate the distributional characteristics of a range of time series models

-- be able to estimate appropriate models of financial and economic time series for the purpose of forecasting and inference

-- be able to apply univariate and multivariate model selection and evaluation methods

-- be able to accommodate conditional heteroskedasticity, unit roots and cointegration in economic and financial time series analysis
Method of teaching and learning 
The module will be delivered by a combination of lectures and tutorials. Lecturers will be designed to provide essential information and introduce students to the basic tools and concepts of time-series analysis. Tutorials will provide students with the opportunity to further develop their skills through the exploration of various theoretical and practical problems, illustrated via actual data sets and real world problems from economics and finance.
Syllabus 
Univariate Time Series Models


Introduction to Time Series Analysis


General ARMA Processes


Stationarity and Unit Roots


Testing for Unit Roots


Estimation of ARMA models


Model Selection


Predicting with ARMA Models


Autoregressive Conditional Heteroskedasticity


Multivariate Time Series Models


Dynamic Models with Stationary Variables


Models with Nonstationary Variables


Vector Autoregressive Models


Cointegration: the Multivariate Case
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26     12      112  150 

Assessment

Sequence Method % of Final Mark
1 Presentation 15.00
2 Group Report 85.00

Module Catalogue generated from SITS CUT-OFF: 10/22/2017 9:37:02 PM