ECON4028 Economic Data Analysis
Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit
ECON4028 Economic Data Analysis
Contents
1. Contact details for lecturers involved in the module
2. Summary of module content
3. Detailed Teaching Arrangements
4. Recommended Reading
5. Assignments for Formative Feedback
6. Module Assessment
7. Data Analysis Software used in the module
8. Detailed EDA project guidelines
8.1 Choice of topic and data
8.2 General Guidance
8.3 Marking Criteria
8.4 Submission Process
8.5 The Word Count
9. Additional Information
9.1 Academic misconduct
9.2 Reading on economics writings
9.3 Autumn Semester University Timetable
1. Contact details for lecturers involved in the module
Name |
Office* |
|
Phone** |
|
|||
Module Convenor, Lectures & Computer Classes |
|||
Sourafel Girma*** |
B7 |
95 15482 |
|
Lectures & Computer Classes |
|||
Marit Hinnosaar*** |
B16 |
84 67492 |
|
Computer Classes |
|
||
Diego Canales |
B43 |
|
* All offices are located in the Sir Clive Granger Building (SCGB)
** Nottingham area code: 0115
*** Office hours by email appointment
2. SUMMARY OF MODULE CONTENT
The Economic Data Analysis (EDA) module is a 15-credit Semester 1 module, which is compulsory for most economics post-graduate students. The module considers a host of techniques for analysing modelling economic data, ranging from data description and visualisation to linear and non-linear modelling of time series, cross-sectional and panel data, both at the micro and macroeconomic levels.
MODULE AIMS:
• Discuss the types of data that are used in Economics
• Develop skills for the insightful exploratory data analysis, that the is description and visualisation economic data.
• Foster skills for appropriate statistical/econometric modelling of economic data and sensible interpretation of results.
• Develop competence for relevant computer software for the management, description and visualisation and modelling economic data.
• Serve as a bridge between introductory econometrics course most students took at undergraduate level and the more advanced and formal econometric courses available in Semester 2.
• Lay solid foundations for the quantitative skills needed to write the dissertation in the summer.
3. DETAILED TEACHING ARRANGEMENTS
CONTACT TIME
Teaching consists of 20 hours of lecture (a combination of online and asynchronous lectures with engagement sessions), and 10 one-hour online computer classes
using the STATA statistical package where you will apply the techniques covered in lectures to various datasets, thereby giving you the opportunity to use statistical software and deepen your practical econometric skills. Please make sure to go through the lecture slides and recordings prior to the tacking the material of the computer classes.
The online lectures and engagement sessions provides you the opportunity to meet your peers in real time, in order to further explore the content of the digital lectures through questions and answers sessions.
LECTURES LINE-UPS: SG: live lectures and MH (pre-recorded lectures + live engagement sessions)
Lecture 1: Introductory lecture on the contents and the module (SG) |
Lecture 2: Exploratory data analysis I- (SG) |
Lecture 3: Exploratory data analysis II: (SG) |
Lecture 4: Exploratory data analysis III - (SG) |
Lecture 5: Revision of basic concepts in regression analysis I (MH) |
Lecture 6: Revision of basic concepts in regression analysis II (MH) |
Lecture 7: Instrumental variables estimation I (MH) |
Lecture 8: Instrumental variables estimation II (MH) |
Lecture 9: Panel data modelling I (MH) |
Lecture 10: Panel data modelling II (MH) |
Lecture 11: Discrete choice modelling I (MH) |
Lecture 12: Discrete choice modelling II (MH) |
Lecture 13: Estimating Average Treatment Effect I (MH) |
Lecture 14: Estimating Average Treatment Effect II (MH) |
Lecture 15: Univariate time series modelling I (SG) |
Lecture 16: Univariate time series modelling II (SG) |
Lecture 17: Vector Autoregression Models (SG) |
Lecture 18: Co-integration analysis (SG) |
Lecture 19: Modelling the volatility of economic and financial time series (SG) |
Lecture 20: Carrying out an economic data analysis project (SG) |
TIMETABLE for ONLINE LIVE LECTURES (Mondays 14- 15pm and Thursdays 13- 14pm): Microsoft Teams code: 841esm3
|
||
Timetable Week |
Date: Week commencing |
Session Topic |
4 |
11/10/2021 |
Live lectures 1 and 2 (SG) |
5 |
18/10/2021 |
Live Lectures 3 and 4 (SG) |
11 |
29/1 1/2021 |
Live lectures 15 and 16 (SG) |
12 |
06/12/2021 |
Live lectures 17 and 18 (SG) |
13 |
13/12/2021 |
Live lectures 19 and 20 (SG) |
TIMETABLE FOR ENGAGEMENT SESSIONS
(Thursdays 13- 14pm):
Microsoft Teams code: 841esm3
|
||
Timetable Week |
Date: |
Session Topic |
6 |
28/10/2021 |
Q&A session on basic concepts in regression analysis based on material of lectures 5-6 (MH) |
7 |
04/10/2021 |
Q&A session on instrumental variables estimation based on material of lectures 7-8 (MH) |
8 |
11/11/2021 |
Q&A session on panel data modelling based on material of lectures 9- 10 (MH) |
9 |
18/11/2021 |
Q&A session on discrete choice modelling based on material of lectures 11- 12 (MH) |
10 |
25/1 1/2021 |
Q&A session on estimating Average Treatment Effect based on material of lectures 13- 14 (MH) |
2021-12-29