Stochastic Dynamic Programming

Prof. Dr. Olaf Posch

Course

Announcements

Dear all,
Unfortunately, I have to cancel the lecture today because of illness. I am trying to find a suitable time/room at another occasion.
Best wishes,
Olaf

Announcement created on: 24/11/2016 07:07

Description

Course objective. This course provides a toolbox for solving dynamic optimization


problems in stochastic macroeconomic models. In particular, we briefly review optimal


control theory and dynamic programming. We then thoroughly study models in discrete


time and continuous time under uncertainty. The optimization problems are illustrated by


various examples of dynamic stochastic general equilibrium (DSGE) models.


Course outline.


Part I: Basic mathematical tools


(i) Control theory (maximum principle, Euler equation, transversality condition)


(ii) Dynamic programming (Bellman equation, envelope theorem, multiple variables)


(iii) An example: Lucas’ model of endogenous growth


Part II: Stochastic models in discrete time


(i) Stochastic control problems


(ii) Analyzing equilibrium dynamics


(iii) An example: Real business cycles (RBC)


(iv) An example: A new Keynesian model for monetary analysis


(v) Solving dynamic equilibrium models with Dynare


Part III: Stochastic models in continuous time


(i) Stochastic differential equations and rules for differentials (Itˆo’s formula)


(ii) An example: Merton’s model of growth under uncertainty


(iii) Stochastic dynamic control problems (Bellman equation)


(iv) An example: Continuous-time RBC (under Gaussian and/or Poisson uncertainty)


(v) An example: The matching approach to unemployment


(vi) An example: W¨alde’s model of endogenous growth cycles

Reading list. Sydsæter, Hammond, Seierstad, and Strøm (2008, chap. 4-12, 290 pages),


Chang (2004, chap. 4, 50 pages), W¨alde (2012), various articles suggested as complementary


material during the course


References


Chang, F.-R. (2004): Stochastic optimization in continuous time. Cambridge Univ. Press.


Sydsæter, K., P. Hammond, A. Seierstad, and A. Strøm (2008): Further Mathematics


for Economic Analysis. Prentice Hall.


Walde, K. (2012): Applied Intertemporal Optimization. Lecture Notes, Gutenberg University


Mainz, http://www.waelde.com/aio.

General Data

  • Abbreviation
    20-109.06
  • Semester
    winter semester 16/17
  • Target Groups
    WiSo doctoral study program
  • Course Type
    lecture
  • Course Language
    English
  • Departments
    Faculty of Economics and Social Sciences

Place and Time

Date
  • Place
    Allendeplatz 2 Hörsaal
  • Time
    at 03/11/2016 from 09:00 to 12:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 03/11/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 10/11/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 17/11/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 409
  • Time
    at 24/11/2016 from 09:00 to 12:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 409
  • Time
    at 24/11/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 01/12/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 409
  • Time
    at 08/12/2016 from 09:00 to 12:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 08/12/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 15/12/2016 from 14:00 to 16:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 22/12/2016 from 09:00 to 12:00
Date
  • Place
    Allendeplatz 1 (Pferdestall) Raum 250
  • Time
    at 22/12/2016 from 14:00 to 16:00

Recognition Modalities

  • Number of Semester Hours
    2
  • Amount of Credit Points
    4
  • Creditable as
    • WiSo doctoral program: WiSo methods for Economics

Registration Modalities

  • Type of Place Allocation
    Manual Place Allocation (after the registration deadline)
  • Information about Registration
  • Max. Number of Participants
    20