APPLIES TO ACADEMIC YEAR 2012/2013
DRE 7004 Topics in Macroeconomics|
Responsible for the course
Hilde C Bjørnland
Department of Economics
According to study plan
Language of instruction
Please note that this course will be revised before it is offered again
This course is divided in two parts. The first part gives an introduction to Computational Methods and Applications in Dynamic General Equilibrium Modeling. The second part covers economic growth theory and studies frontier research on topics in economic growth.
After taking this course the students should have a solid knowledge of numerical methods used to solve problems in macroeconomics and understand basic and advanced models of endogenous growth. The studens should master and produce sophisticated research on dynamic general equilibrium models and models of economic growth.
Admission to a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of admission to a PhD programme when signing up for a course with the doctoral administration. Candidates can be allowed to sit in on courses by approval of the course leader. Sitting in on courses does not permit registration for courses, handing in exams or gaining credits for the course. Course certificates or conformation letters will not be issued for sitting in on courses
Acemoglu, Daron. 2009. Introduction to modern economic growth. Princeton University Press
Heer, Burkhard and Alfred Maussner. 2009. Dynamic general equilibrium modeling : computational methods and applications. 2nd ed. Springer
A complete list of articles and book chapters will be distributed during the first meeting or prior to the course.
(i) Introduction, Linear Algebra Boot Camp and Markov Chains
(ii) Numerical Integration and Function Approximation
(iii) Dynamic Programming and Parameterized Expectations and/or Projection Methods
(iv) Introduction to basic and advanced models of endogenous growth
The course uses modern programming software such as MATLAB.
Learning process and workload
Workload (6 ECTS)
Lectures 30 hours
Specified learning activities (including reading) 75 hours
Autonomous student learning (including exam preparation) 75 hours
Total 180 hours
Course structure and grading:
The course will we taught in 10 intensive modules. Each module consists of 3 hours.
Students are required to participate in class – both in discussions and by presenting models/material from the reading lists – as well as solve and hand in solutions to exercises and problems.
The final grade is pass/fail. 30 hours home exam.
DRE 70041 accounts for 100% of the grade
Examination support materials
Re-takes are only possible at the next time a course will be held. When the course evaluation has a separate exam code for each part of the evaluation it is possible to retake parts of the evaluation. Otherwise, the whole course must be re-evaluated when a student wants to retake an exam.
Academic honesty and trust are important to all of us as individuals, and represent values that are encouraged and promoted by the honour code system. This is a most significant university tradition. Students are responsible for familiarizing themselves with the ideals of the honour code system, to which the faculty are also deeply committed.
Any violation of the honour code will be dealt with in accordance with BI’s procedures for cheating. These issues are a serious matter to everyone associated with the programs at BI and are at the heart of the honour code and academic integrity. If you have any questions about your responsibilities under the honour code, please ask.
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