[Forside] [Hovedområder] [Perioder] [Udannelser] [Alle kurser på en side]
LEARNING OBJECTIVES:
Having completed this course, the student will be able to
• reflect on advanced dynamic models
• reflect on solution techniques in discrete and continuous-time
• theorize on dynamic stochastic optimal control problems
• reflect on models of optimal saving under uncertainty
• relate basic concepts of probability theory
• apply numeric solution techniques to continuous-time macroeconomic models
COURSE DESCRIPTION:
This course provides a toolbox for solving dynamic optimization problems in (stochastic) economic models. In particular, we review most of the mathematical tools required for typical graduate courses in economics including the calculus of variations, optimal control theory, and dynamic programming. We then thoroughly study models in discrete and continuous time under uncertainty. Throughout the course, the optimization problems are illustrated using various examples. A particular focus will be on formulating and solving dynamic models in modern macroeconomics.
COURSE SUBJECT AREAS:
Part I: Basic mathematical
(i) Topics in integration and differential equations (rules of transformation, differentiation, solution techniques)
(ii) Qualitative theory (phase plane analysis, stability for nonlinear systems)
(iii) Calculus of variation (Euler equation, transversality condition)
(iv) Control theory (maximum principle, variable final time, infinite horizon)
(v) Dynamic programming (Bellman equation, envelope theorem, multiple control and state variables)
(vi) `Dynamic macroeconomic modeling with Matlab' on numerical solutions to continuous-time dynamic macroeconomic models.
(vii) Basic concepts of probability theory (probability model, stochastic processes, functions of random variables)
Part II: Stochastic models in discrete time
(i) Topics in difference equations (basic concepts, solution techniques)
(ii) Stochastic difference equations (state-space representation, indeterminacy)
(iii) Discrete-time optimization (dynamic programming, stochastic control problems)
(iv) Overlapping-generations, multi-period models (real-business-cycles)
Part III: Stochastic models in continuous time
(i) Stochastic differential equations and rules for differentials (Itô's formula)
(ii) Solution techniques (Option pricing)
(iii) Stochastic dynamic control problems (Bellman equation)
(iv) Optimal saving under uncertainty (Poisson uncertainty, Brownian motions)
(v) Matching approach to unemployment
(vi) Dynamic stochastic general equilibrium models
REQUIRED COURSES:
3505: Macro 1
LECTURER: Olaf Posch.
TEACHING METHOD:
Lectures and take-home problem sets.
English
LITERATURE:
Reading list: The main text books are Sydsaeter et al. (2008, chap. 4-12, 290 pages) and Chang (2004, chap. 4, 50 pages). Readings of Stokey et al. (1989), Ljungqvist and Sargent (2004), Spanos (1999), and Wälde (2009) as well as articles will be suggested as complementary material during the course. Reflecting the lecture notes is compulsory.
Lecture notes: approx. 120 pages
Total: approx. 460 pages
FORM OF ASSESSMENT: Take-home exam.
- It is a prerequisite that four mandatory problemsets are handed in to the lecturer and are approved before the take-home exam.
EXAMINATION AIDS ALLOWED: All