Applied Econometrics

Course 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.

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
MSc Programme in Sustainable Forest and Nature Management

 

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.

lectures, own reading, exercises, computer laboratory work, and work with case-reports

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.

It is absolutely necessary to have passed a course in statistics and econometrics with competences corresponding to:

- LMAB10069 Statistical Data Analysis 1
- NIFB14014U Econometrics

Academic qualifications equivalent to a BSc degree is recommended.

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.

 

ECTS
7,5 ECTS
Type of assessment
Written examination, 4 hours under invigilation
Type of assessment details
the written examination counts 100% of the grade.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

See Learning Outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 36
  • Preparation
  • 70
  • Practical exercises
  • 36
  • Project work
  • 60
  • Exam
  • 4
  • English
  • 206

Kursusinformation

Language
English
Course number
LOJK10272U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 1
Schedulegroup
B
Capacity
No restrictions
The number of seats may be reduced in the late registration period
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
  • Sinne Smed   (6-6d7c74423a3d4a6b767f77787338757f386e75)
Teacher

Sinne Smed
Laura Mørch Andersen

Saved on the 28-02-2022

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