# LOJK10272U Applied Econometrics

Volume 2020/2021
Education

MSc Programme in Agricultural Economics

MSc Programme in Environment and Development

MSc Programme in Environmental and Natural Resource Economics

MSc Programme in Forest and Nature Management

Content

This course aims at providing the student basic knowledge about relatively advanced regression models and methods that are relevant to applied economists. With a mix of econometric theory and applications the course will develop the student's skills to conduct own empirical research projects.

Learning Outcome

The main objective of the course is to provide an introduction to the more advanced themes in econometric modeling with an emphasis on application of estimation techniques and statistical testing.
After completing the course it is expected that the student is able to:

Knowledge:
- Reflect about the appropriate choice of estimator given certain types of data such as panel data, data with a binary dependent variable and other types of limited dependent variables.
- Reflect about econometric problems and solutions in relation to endogenous regressors.

Skills:
- Formulate, estimate and interpret results of multiple linear regression models.
- Formulate, estimate and interpret results of econometric models for binary dependent variables.
- Formulate, estimate and interpret results of econometric models for corner solution responses. (Only corners eq. to zero)
- Formulate, estimate and interpret results of econometric models for count data.
- Formulate, estimate and interpret results of linear econometric models for panel data.
- Formulate, estimate and interpret results of linear econometric models with endogenous regressors.

Competencies:
- Understand the concepts of consistency, unbiasedness and asymptotic normality of estimators.
- Understand the concept of prediction, and understand that the calculation of expected values varies between models.
- Understand the concept of endogeneity
- Discuss the results of econometric analyses based on model assumptions and limitations.
- Interpret outcomes of econometric analyses and draw appropriate conclusions.

Jeffrey M. Wooldridge. Introductory Econometrics: EMEA Adaptation

Software: R

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

competences corresponding to
LMAB10069 Statistical Data Analysis 1
LOJB10242 Thematic Course: Applied Economic Analysis

Academic qualifications equivalent to a BSc degree is recommended.
lectures, own reading, exercises, computer laboratory work, and work with case-reports
• Category
• Hours
• Lectures
• 36
• Preparation
• 70
• Practical exercises
• 36
• Project work
• 60
• Exam
• 4
• Total
• 206
Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
• The students get individual written feedback on the three assignments handed in during the course, and joint verbal feedback on each assignment.
• During exercises the students get verbal feedback on their methods and interpretations of results
• The students get verbal feedback on the learing targets during the joint summing up exercises in class.

Credit
7,5 ECTS
Type of assessment
Written examination, 4 hours under invigilation
the written examination counts 100% of the grade.

The course has been selected for ITX exam on Peter Bangs Vej.
Exam registration requirements

During the block students will work on the completion of 3 applied case studies where econometric analysis has to be used to analyse different data problems and assess the quality of the results. The reports can be done as group work. Each of the reports must be passed to allow the student to take the final exam.

Aid
All aids allowed

The University will make computers and power available to students taking written exams with invigilation in the University’s building on Peter Bangs Vej 36 (ITX). Students are therefore not permitted to bring their own computers, tablets or mobile phones. If textbooks and/or notes are permitted, according to the course description, these must be in paper format or on a USB flash drive.

Marking scale