ECON30530

Learning Outcomes:

At the end of this course, students should be able to:
1. recognize econometric problems that may arise from data or hypotheses,
2. understand the limitations of OLS and how they need to be corrected;
3. identify the appropriate estimator and its properties;
4. estimate the model using R;
5. discuss and interpret the results.

Indicative Module Content:

1. Review of basic regression models
2. Panel data models
3. Limited dependent variables models
4. Treatment effects models

Student Effort Hours:

Approaches to Teaching and Learning:

The modules comprises lectures and tutorials. The lectures will introduce the theory and background of the different estimators and econometric problems we encounter and start introducing the empirical implementation. The tutorials allow students to apply the techniques learned on real data and to develop confidence in handling datasets and statistical software, building and estimating models, and interpreting results correctly.

AI may only be used in this module only to generate or check code in R (or any other coding language you are using), 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) were 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 without warning to determine the authenticity of the work. Please refer to the UCD Student Academic Misconduct Procedure for more.

Requirements, Exclusions and Recommendations

Learning Recommendations:

It is strongly recommended that students have passed ECON30130 or an equivalent undergraduate econometrics module.

Module Requisites and Incompatibles

Incompatibles:
ECON3018J - Advanced Econometrics, ECON30420 - Advanced Econometrics


 

Assessment Strategy

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

1. Two projects will be assigned throughout the semester and solutions will be posted on Brightspace. 2. Appointments will be given to those students wishing to get individual feedback on the empirical assignments and the final examination.

Wooldridge, Introductory Econometrics: A Modern Approach. Cengage. All recent editions are all fairly similar.
Stock and Watson, Introduction to Econometrics. Pearson. All recent editions are all fairly similar.
Both of these books include guindance on computer-based implementation in STATA.

For R specific econometrics:
Florian Heiss, Using R for Introductory Econometrics. 2nd edition. https://www.urfie.net/
Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer, Introduction to Econometrics with R. https://www.econometrics-with-r.org/

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.