Teaching

These lecture notes are meant to provide a first intuitive introduction to continuous-time methods. Consult the standard textbooks (Karatzas & Shreve, Øksendahl) if you are looking for more.

• Deterministic control: Hamilton-Jacobi-Bellman equation (HJBs) etc. (+homework)

• Stochastic differential equations (SDEs), the Ito Rule and HJBs (+homeworks on Ito Rule and SDEs & PDEs)

• Kolmogorov Forward Equation (+homework)

• Numerical techniques: the trinomial grid (no homework; see also the computational appendix to Barczyk & Kredler, QE 2014)

• Tricks for exit times, exit probabilities, and densities (with an explanation of Green Functions) (homeworks included in notes)

This is a course I taught from 2008 to 2021 in the first year of Carlos III's Ph.D. program.

Lecture notes (thanks to Sergio Feijoo, my long-time teaching assistant, for typing these up!):

1. Dynamic Programming under Certainty

2. Dynamic Programming under Uncertainty

3. Continuous-Time Dynamic Programming

4. Solving DSGE Models: Linearization

5. The New-Keynesian Model

6. Search and Matching

(problem sets, syllabus)

see UC3M's Aula Global