Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: ECO205
Module Title: Econometrics I
Module Level: Level 2
Module Credits: 5.00
Academic Year: 2017/18
Semester: SEM1
Originating Department: International Business School
Pre-requisites: N/A
   
Aims
Econometrics I is concerned with the testing of economic theory using real world data. This module introduces the subject by focusing on the principles of Ordinary Least Squares regression analysis, which is the cornerstone of econometrics. The module will provide practical experience via regular laboratory session.
Learning outcomes 
By the end of the module you will be able to:

1. explain the nature and classical methodology of econometrics

2. estimate and interpret bivariate regression models using formulas and econometric software

3. estimate and interpret multivariate regression models using econometric software

4. explain the assumptions underpinning valid estimation and inference in regression models

5. explain the consequences of violations of assumptions and what tests and remedies are available to detect and deal with violations of assumptions

6. formulate and conduct tests of hypotheses using regression models

7. formulate models incorporating dummy variables and explain their interpretation

8. extend the least Square principles to deal with nonlinear population regression functions.

9. have a good understanding of the use of instrumental variables

10 understand and make use of robust standard errors


Method of teaching and learning 
The module will be taught using a combination of lectures, computer lab and directed study. The lectures are intended to provide an introduction to the topics covered in the syllabus. This will be built upon by practical experience using econometric models in laboratory sessions and the regular completion of structured exercises. Learning will be reinforced by appropriate readings from the course text.
Syllabus 
1. Review of statistics and sampling distribution

2. Bivariate regression: Introduction to OLS

3. OLS assumptions and small samples

4. Bivariate regression: Hypothesis Testing and robust standard errors

5. Multiple regression: Assumptions and properties

6. Multiple regression: Hypothesis Testing

7. Alternative measures of fit

8. Nonlinear Regression: logarithms

9. Nonlinear Regression: Dummy variables

10. Heteroskedasticity

11. Introduction to Instrumental Variables












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 Written Examination Paper 85.00
2 Mid-Term Exam 15.00

Module Catalogue generated from SITS CUT-OFF: 10/22/2017 9:31:31 PM