Module Catalogues, Xi'an Jiaotong-Liverpool University

Module Code: ECO205
Module Title: Econometrics I
Module Level: Level 2
Module Credits: 5.00
Semester: SEM1
Originating Department: International Business School Suzhou
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 A. explain the nature and classical methodology of econometricsB. estimate and interpret bivariate regression models using formulas and econometric softwareC. estimate and interpret multivariate regression models using econometric softwareD. explain the assumptions underpinning valid estimation and inference in regression modelsE. explain the consequences of violations of assumptions and what tests and remedies are available to detect and deal with violations of assumptionsF. formulate and conduct tests of hypotheses using regression modelsG. formulate models incorporating dummy variables and explain their interpretationH. extend the least Square principles to deal with nonlinear population regression functionsI. have a good understanding of the use of instrumental variablesJ. 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 distribution2. Bivariate regression: Introduction to OLS3. OLS assumptions and small samples4. Bivariate regression: Hypothesis Testing and robust standard errors5. Multiple regression: Assumptions and properties6. Multiple regression: Hypothesis Testing7. Alternative measures of fit8. Nonlinear Regression: logarithms9. Nonlinear Regression: Dummy variables10. 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 Mid-Term Exam 15.00 2 Group Coursework 15.00 3 Final Exam 70.00
 Module Catalogue generated from SITS CUT-OFF: 6/6/2020 9:43:47 PM