Learning Outcomes:
By the end of this module students should be able to:
1. describe the properties and shortcomings of a variety of econometric models and estimators,
2. apply the methods analyzed in class on macro and finance data.
Curricular information is subject to change.
This module introduces students to some of the most commonly used time series econometrics models and estimation methods in the field of macro economics and finance. We will explore univariate and multivariate time series (ARMA, unit roots, VAR, etc.).
This module assumes that students have passed an introductory econometrics module such as ECON30130 or equivalent.
This module assumes that students have passed an introductory econometrics module such as ECON30130 or equivalent.
About this Module
Student Effort Hours:
| Student Effort Type | Hours |
|---|---|
| Autonomous Student Learning | 80 |
| Lectures | 22 |
| Computer Aided Lab | 10 |
| Total | 112 |
Approaches to Teaching and Learning:
The modules comprises lectures and hands-on computer lab sessions; the latter allow students to apply the techniques learned on real data and to develop confidence in handling datasets and statistical software.AI may be used in this module to generate or check code in R, Stata, etc. However, you must understand how that code operates, be able to explain it, accept responsibility for it, and explicitly acknowledge that AI was used and in what ways it was used. You are also required to provide a reference for the software used, and what prompts (if any) where used. Here is an example of a citation:
Code generated by ChatGPT, March 31, 2024, OpenAI, https://chat.openai.com.
The following prompts were used: “XYZ”.
The use of AI-generated content without explicit attribution is a form of academic misconduct. Please note that if academic misconduct is suspected, you may be asked to discuss or explain components of your assignment to determine the authenticity of the work. Please refer to the UCD Student Academic Misconduct Procedure for more.
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Incompatibles:ECON3007J - Econometrics of Financial Mark, ECON30420 - Advanced Econometrics, STAT30010 - Time Series Analysis, STAT3007J - Time Series Analysis
Assessment Strategy
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Individual Project: Individual project involving data analysis | Week 7 | Alternative linear conversion grade scale 40% | No | 30 |
No |
| Exam (In-person): Final exam during end of trimester exam period | End of trimester Duration: 2 hr(s) |
Alternative linear conversion grade scale 40% | No | 70 |
No |
| Resit In | Terminal Exam |
|---|---|
| Spring | Yes - 2 Hour |
Feedback Strategy/Strategies
• Group/class feedback, post-assessment
• Self-assessment activities
How will my Feedback be Delivered?
1. Regular problem sets will be assigned throughout the semester for self-assessment; solutions will be posted on Brightspace and will be explained in detail during tutorials. 2. Appointments will be given to those students wishing to get individual feedback on the empirical assignments and the final examination.
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
| Autumn | Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Fri 13:00 - 13:50 |
| Autumn | Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Tues 14:00 - 14:50 |
| Autumn | Tutorial | Offering 1 | Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 | Fri 10:00 - 10:50 |