Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: ECO401
Module Title: Econometrics
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2020/21
Semester: SEM1
Originating Department: International Business School Suzhou
Pre-requisites: N/A
   
Aims
The goal of this module is to provide students with a solid foundation in the statistical and econometric techniques that allow them to conduct independent empirical investigations in economics and finance. The approach centers on the regression methods, including their use in estimating and testing the validity of models in economics and finance. The module serves as a guide for business managers, research economists and practitioners. The aims are that students will: (i) acquire “hands on” training in the use, presentation and interpretation of economic data; (ii) understand aspects of the theories and principles of econometric analysis in economics and finance, (iii) be aware of a range of inferential techniques commonly employed in econometrics, and (iv) understand the limitations of such techniques in different circumstances.
Learning outcomes 
A Demonstrate in-depth knowledge and understanding of OLS assumptions and of their violations

B Critically evaluate and apply a range of mathematical and statistical techniques necessary for understanding and using econometric methodology.

C Formulate, estimate and test a wide range of models in empirical analyses.

D Use the econometric package STATA in real applications.


Method of teaching and learning 
There will be a two-hour lecture per week that covers some of the most important topics in modern econometrics, for doing and understanding empirical work. Our focus is mainly on the applied aspect of econometrics; that is, the module does not concentrate too much on the formulae behind each technique nor on formal proofs, but on the intuition behind the approaches and their practical relevance. In addition, the students will make use of the computing facilities and software for the analysis of real data and problems in the lab sessions. STATA is used to computing practical in which students will apply the theories learned in lectures. Reference to journal articles will expose students to both applications of, and developments in, techniques in the areas they will be studying.
Syllabus 
1. Introduction, Review of concepts

2. OLS, Regression, Goodness of fit

3. Hypothesis testing, Asymptotic properties

4. Interpretation of models, Misspecifications

5. Functional forms

6. Heteroscedasticity, testing

7. Autocorrelation, testing

8. Tests and Misspecifications

9. Instrumental variables estimation

10. Moment conditions

11. Panel data modeling
Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26     6      118  150 

Assessment

Sequence Method % of Final Mark
1 Mid-Term Exam 20.00
2 Final Exam 80.00

Module Catalogue generated from SITS CUT-OFF: 6/3/2020 1:45:21 AM