Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: ECO214
Module Title: Econometrics II
Module Level: Level 2
Module Credits: 5.00
Academic Year: 2018/19
Semester: SEM2
Originating Department: International Business School
Pre-requisites: N/A
   
Aims
Econometrics II is concerned with the testing of economic theory using real world data. This module is complimentary to Econometrics I, expanding the principles of Ordinary Least Squares regression analysis when dealing with panel data and time series data. The module will provide students with practical experience via regular laboratory session.
Learning outcomes 
A. expand the framework of multiple regressions to a panel data setting

B. expand the framework of multiple regressions to time series models

C. conduct regression analysis and make inferences using formal language

D. demonstrate a basic understanding of advanced econometrics, including the VAR model, co-integration, and the VECM model

E. apply clustered standard errors and the HAC standard errors

F. formulate, estimate and conduct tests of hypotheses using time series data
G. conduct forecasts in time series models

H. test for Granger causality in ADL or VAR models

I. test for structural breaks in time series using the Chow test or the QLR test

J. choosing between different flavors of the ADF model to test the presence of stochastic trend in time series


Method of teaching and learning 
We follow the same approach than Econometrics 1. The module will be taught using a combination of lectures, computer lab and directed study. The lectures provide an introduction to the topics covered in the syllabus. This will be built upon by practical experience in laboratory sessions and structured exercises. Learning will be reinforced by appropriate readings from the module text
Syllabus 
1. Review of multiple regressions


2. Panel data I


3. Panel data II


4. Introduction to time series and forecasting


5. Nonstationarity I: unit roots


6. Nonstationarity II: structural breaks


7. Dynamic causal effect in time series


8. Bivariate VAR and cointegration


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 Midterm Exam 15.00
2 Group Presentation 15.00
3 Final Exam 70.00

Module Catalogue generated from SITS CUT-OFF: 5/26/2018 10:17:53 AM