# Introduction to Econometrics

### Course content

The course will be held online.

The course consists of a limited number of lectures, a larger number of teacher-made but self-organized exercises and a lot of independent work and self-study. Learning how to apply econometrics to interesting economic problems quite naturally entails working with economic data. Therefore, a large part of the course focuses on acquainting students with the R programming language, the success of which largely depends on the time and effort the students spend on it. The final learning outcome is therefore closely linked to the students’ ability to work independently and thoroughly with the supplied material.

The course focuses on introducing the linear regression model for data analysis within economics. Emphasis is on the statistical theory behind econometrics, understanding the nature of economic data, and the applications of econometrics to real-world problems. The latter emphasizes a focus on the interpretation of statistical results and a discussion on possible limitations or issues with the chosen application. More formally, this requires a thorough understanding of the assumptions underlying the linear regression model and what to do when these assumptions are violated.

The course is very much an applied econometrics course in the sense that it focuses on using and discussing which econometric approach would best uncover the causal relationship of interest, to a larger extent than deriving properties of estimators (or similar).

A natural part of applying statistical methods to some real-world problem is working with data. Therefore, parts of the curriculum focus explicitly on good practices when managing and collecting economic data. Furthermore, the course continuously works with practical data examples, as these are a necessity when trying to infer anything meaningful about some economic phenomenon.

Learning outcome

The purpose of the course is to prepare students to future courses in econometrics. The aim therefore includes introducing the application of statistical methods and models to relevant economic problems using real-world data.

After completing the course, the student should be able to:

Knowledge:

• Explain the basic econometric concepts related to the linear regression model
• Explain the assumptions behind the linear regression model that ensures OLS is a consistent, unbiased, and efficient estimator

Skills:

• Carry out an OLS estimation of parameters in a (multiple) linear regression model
• Report and interpret results of linear econometric models for continuous, discrete, binary, and dummy variables
• Apply relevant econometric methods to a chosen economic problem
• Carry out, explain, and interpret hypothesis tests for specific parameter restrictions and correct model specification in a (multiple) linear regression setting

Competences:

• Interpret the results of an econometric analysis and make relevant conclusions or policy recommendations based upon these
• Use relevant econometric methods to investigate a specific economic problem
• Discuss whether the relevant assumptions behind the linear regression model apply to some situation and the implications hereof
• Carry out a basic econometric analysis independently, from data collection to policy implications, using appropriate linear regression tools

The course is online. The course is a combination of (short) lectures, exercises, and independent work. The (short) lectures will delve into key concepts, whilst exercises provide hands-on experience with core material and R. Students’ learning outcome is largely dependent on dynamic participation and effort spent on working with the supplied exercises and other course material.

Throughout the course, students’ will be able to collaborate on exercises and ask questions to all of the supplied material. Quizzes for the individual readings will be provided as a way to follow up on student learning. This dynamic is also the basis for the (short) lectures.

Literature: Jeffrey M. Wooldridge. Introductory Econometrics: A Modern Approach, 7th edition

Software: R

The literature is indicative. The exact literature will be announced at Absalon at the beginning of the course.

Competencies equivalent to those gained from LMAB10066U Matematik og databehandling (MatDat) and LMAB10069 Statistisk dataanalyse 1 or similar.

The course curriculum is identical to that of LOJB10283U Econometrics.

Oral
Individual
Collective
Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Written assignment, 72 hours
Type of assessment details
To pass the course the student should be able to: Explain the basic concepts behind the linear regression model, use data to analyze a specific economic problem, perform estimations and hypothesis tests in a linear regression setting, interpret the ensuing results, and make relevant conclusions based upon these
Aid
All aids allowed
Marking scale
Censorship form
No external censorship
One internal examiner
Re-exam

Oral examination, 20-30 minutes with no preparation and no aids allowed.

##### Criteria for exam assessment

See Learning Outcome

Single subject courses (day)

• Category
• Hours
• Lectures
• 20
• Preparation
• 84
• Theory exercises
• 30
• Exam
• 72
• English
• 206

### Kursusinformation

Language
English
Course number
NIFB22001U
ECTS
7,5 ECTS
Programme level
Bachelor
Placement
Summer
Schedulegroup
4-22 August 2025
3-week online summer course. Teaching in the period 4-15 August 2025. The following week there is a 72 hour take-home exam from Tuesday to Friday.
Capacity
No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Studyboard
Study Board of Natural Resources, Environment and Animal Science
##### Contracting department
• Department of Food and Resource Economics
##### Contracting faculty
• Faculty of Science
##### Course Coordinator
• Carl-Emil Pless   (3-656772426b687471306d7730666d)
##### Teacher

Carl-Emil Pless

Saved on the 04-09-2024

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