Courses Bachelor Display 2022-2023

Course Description To PDF
Course title Introduction to Software in Econometrics
Course code EBS2072
ECTS credits 4,0
Assessment Whole/Half Grades
Period
Period Start End Mon Tue Wed Thu Fri
3 16-1-2023 27-1-2023 C
Level Advanced
Coordinator Nalan Bastürk
For more information: n.basturk@maastrichtuniversity.nl
Language of instruction English
Goals
1. The student will learn to model simple econometric problems in the Bayesian framework.
2. The student will learn how to implement simulation-based Bayesian inference procedures for standard econometric models in the statistical programming software R and how to interpret estimation results.
3. The student will learn how to assess the appropriateness and accuracy of different simulation methods in different examples.
Description
PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.

Students will learn basic principles of Bayesian inference in econometrics focusing on computational techniques. They will acquire the skills to implement simulation based Bayesian inference procedures for standard econometric models in the statistical programming software R. Being able to implement and apply simulation based statistical methods is fundamental for the application of Bayesian methods to econometric problems.
Literature
Prerequisites
Econometric Methods 1 (EBC2111)
Keywords
Teaching methods (indicative; course manual is definitive)
Assessment methods (indicative; course manual is definitive)
Evaluation in previous academic year For the complete evaluation of this course please click "here"
This course belongs to the following programmes / specialisations
Bachelor Econometrics and Operations Research Year 3 Elective Skill(s)