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# ECON30130

#### Econometrics: Applying Statistics to Economic Data (ECON30130)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Trimester:
Autumn and Spring (separate)
Mode of Delivery:
On Campus
Internship Module:
No

Curricular information is subject to change.

Econometrics is the essential statistical toolbox for economists. Much of economic research, and many jobs in industry and government, require economists to analyse data. The purpose of this course is to teach students the basic concepts of econometrics. We will introduce linear regression, which is arguably the most important tool in econometrics, and learn how this tool can be used to quantify all sorts of economic relationships. The course gives students a solid theoretical foundation and teaches them how to apply the methods in their own work.

###### 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, they 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

- Binary dependent variables
- Introduction to causal inference
- Introduction to prediction

###### Student Effort Hours:
Student Effort Type Hours
Lectures

22

Computer Aided Lab

10

Autonomous Student Learning

75

Total

107

###### 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
Learning Requirements:

Students are required to have completed ECON 20040 Statistics for Economists, MIS 10010
Quantitative Analysis for Business, or an equivalent module on Basic Statistics and Probability.

Learning Recommendations:

It is recommended that students have an understanding of the basic Principles of Microeconomics and Principles of Macroeconomics such as ECON 10010 & ECON 10020

###### Module Requisites and Incompatibles
Incompatibles:
ECON3002J - Applied Econometrics, ECON42470 - Econometrics HDip

###### Assessment Strategy Invalid Option
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Quizzes/Short Exercises: A series of six in-class pop quizzes. Each quiz will take a few minutes, and your highest five scores will count towards your grade. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Graded No
15
No
Assignment(Including Essay): This assignment will require students to analyse particular datasets & answer a number of questions based on the results. Students may work in groups of up to three (3) people. Week 8, Week 11, Week 12 Alternative linear conversion grade scale 40% No
25
No
Exam (In-person): Final exam End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No
60
No

###### Carry forward of passed components Invalid Option
No

Remediation Type Remediation Timing
Repeat Within Two Trimesters
###### 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.

The main textbook is

James H. Stock & Mark W. Watson, Introduction to Econometrics, published by Pearson. The book is currently in its 4th edition; previous editions are fine.
Name Role
Mr Simone Arrigoni Tutor
Ms Manvi Jindal Tutor
Yung-Shiang Yang Tutor
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) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Thurs 16:00 - 17:50
Autumn Computer Aided Lab Offering 1 Week(s) - 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Fri 12:00 - 12:50
Autumn Computer Aided Lab Offering 2 Week(s) - 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Tues 14:00 - 14:50
Autumn Computer Aided Lab Offering 6 Week(s) - 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Tues 17:00 - 17:50
Autumn Computer Aided Lab Offering 12 Week(s) - 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Fri 13:00 - 13:50
Autumn Computer Aided Lab Offering 14 Week(s) - 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Tues 13:00 - 13:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 16:00 - 17:50
Spring Computer Aided Lab Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 14:00 - 14:50
Spring Computer Aided Lab Offering 2 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 15:00 - 15:50