ECON42470

Learning Outcomes:

Upon successful completion of the course, a student will have the ability to perform linear regression; to formally test statistical hypotheses and to evaluate empirical economic research. In addition, he/she will have an appreciation of the strength and weaknesses of econometrics and its use in the evaluation of competing economic theories and alternative policies.

Indicative Module Content:

Core topics:

1) Statistics refresher
2) Linear regression with one regressor
3) Linear regression with multiple regressors
4) Non-linear models

Advanced topics (time permitting)
- Binary dependent variables
- Introduction to causal inference
- Introduction to prediction

Student Effort Hours:

Approaches to Teaching and Learning:

We will have two hours of lecture per week where we will learn the theory behind econometric analysis.
In weekly computer labs, students learn to apply the theory to data using Stata or R.

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) 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 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:
ECON30130 - Econometrics

Additional Information:
This module can only be taken by students who can show they have taken an undergraduate course in statistics and have achieved a good understanding of this subject (that is a grade equivalent to 60% or above).


 

Assessment Strategy

 

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

Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Advice will be provided to the class on what made for good and not-so-good projects/assignments with relevant examples.