Applied Econometrics II
| Code | School | Level | Credits | Semesters |
| ECON2033 | School of Economics | 2 | 20 | Spring China |
- Code
- ECON2033
- School
- School of Economics
- Level
- 2
- Credits
- 20
- Semesters
- Spring China
Summary
The module introduces (a) time series analysis, (b) cross-section regressions with binary dependent variables, and (c) panel data analysis. Part (a) covers dynamic models, serial correlation, forecasting and (co)integration; part (b) studies the linear probability model, as well as logit and probit regressions; part (c) introduces basic panel econometrics covering the fixed effects and random effects models. Students will enhance their understanding of the material covered in the lectures via hands-on computer classes using Stata and in tutorials covering worked examples.
The pre-requisite for this module is ECON2032 Applied Econometrics I.
Please note: This module is assessed at the end of Spring semester. First sit/ Re-sit exams are scheduled normally in the summer and can take the same form as the missing/ failed component of the assessment (exam, essay etc.) or other form, as decided by the School.
Target Students
Year 3 Economics students.
Classes
- One 2-hour lecture each week for 11 weeks
- One 1-hour computing each week for 6 weeks
Assessment
- 100% Exam 1 (2-hour): 2 hours written exam
Assessed by end of spring semester
Educational Aims
The module aims to:Introduce students to the principles, uses and interpretation of regression techniques and testing procedures most commonly employed in applied economics using time series data, cross-section models with a binary dependent variable, and simple panel data models;Provide students with sufficient knowledge of regression methods to critically evaluate and interpret published empirical research;Enable students to undertake empirical analyses and econometric forecasting using specialist econometric software.The module aims to enable the participants to develop:Co-ordination with others through problem-based group work;Digital capabilities through an ability to understand verbal, graphical, mathematical and econometric representation of ideas and analysis.Learning Outcomes
On completing this module, the learning outcomes are such that students will be able to:
Knowledge and Understanding:
A4 demonstrate understanding of verbal, graphical, mathematical and econometric representation of economic ideas and analysis, including the relationship between them
A5 show understanding of relevant mathematical and statistical techniques
Intellectual Skills:
B3 Be able to evaluate, analyze and present quantitative data
Professional / Practical Skills:
C2 Understand the sources and content of economic data and evidence, as well as appropriate methods of analysis
Transferable Skills:
D1 Apply mathematical, statistical and graphical techniques in an appropriate manner
D3 Use appropriate IT packages effectively.