This graduate-level course teaches state-of-the art techniques to solve and analyse advanced models.
We will cover models with heterogeneous agents, continuous time models, and models with occasionally binding
constraints, specifically models in which the economy can be at the zero lower bound for the policy interest rate.
In addition to teaching techniques, the course also focuses on practical problems that researchers run into when
using these methods. These courses are aimed atgraduate students and academics.
knowledge about dynamic models, e.g., know what an Euler and a Bellman equation are
Knowledge on programming with Matlab
Knowledge on solving dynamic models with a representative agent like Dynare and value function iteration
Knowledge of the Kalman filter
Key elements
Each morning: 3 hour lecture
Each afternoon: computational assignments solved in groups with assistance provided by instructor and teaching assistants
Not a focus on one technique, but discussion of state of the art available alternatives
Focus on accuracy - making sure that what you get makes sense
Not just computational techniques; also links to economic problems
Focus on understanding the techniques, not on simply running programs and generating output
Lecture notes and programs with which you can do the assignments and improve your skills after the course has ended
Wouter den Haan, the main instructor, has taught at Carnegie Mellon University, University of California at San Diego,
Wharton School of the University of Pennsylvania, London Business School, London School of Economics, Stockholm School of
Economics, Mannheim University, CREST-ENSAE, Universitat Pompeu Fabra, Koc University, Bank of Portugal, Riksbank in
Stockholm, and the European Central Bank.
Course Outline
Monday-Tuesday: Solving and simulating models with heterogeneous agents
Overview
We will look at popular algorithms used to solve models with heterogeneous agents and with aggregate risk.
We will go through their implementation and discuss their strengths and weaknesses. The pioneering algorithm of
Krusell and Smith (1998) is often reliable, but it is also quite slow and we will discuss improvements. In
particular, we will discuss how to efficiently compute a stochastic simulation which avoids sampling uncertainty,
and we will discuss alternative techniques which avoids simulation all together. We will discuss ways to impose
market clearing, which in some applications is a non-trivial and important issue. we will teach you certain "tricks"
to deal with this. We will also discuss how to deal with portfolio problems, asset pricing, and the introduction of
money in these types of models. Lastly, we will discuss how to exploit linearization techniques when issues like the
zero lower bound are present.
Topics
Simulation and distributions
Krusell & Smith algorithm to solve models with heterogeneous agents and aggregate uncertainty
Avoiding sampling uncertainty
Xpa algorithm to solve models with heterogeneous agents aggregate uncertainty
Obtaining the ergodic distribution quickly (without simulating) as the Eigenvector of the matrix in the transition equation
Applications & exercises
Solving models with heterogeneous agents.
Wednesday-Thursday: Continuous time models
Overview
Most macroeconomic analysis takes place in discrete time. But some problems are better dealt with in continuous time.
These two days we focus on continuous-time models and explore numerical algorithms to solve them. During these days,
we will discuss how to exploit linearization techniques in a smart way and deal with occasionally binding constraints
such as the zero-lower-bound in monetary models.
Friday: Occassionally binding constraints and bounded rationality
Overview
Learn different methods to deal with occasionally binding constraints and an introduction to models in
which agents do not have rational expections and either use heuristics or learn.
Applications & exercises
Solve a model with an occasionally binding constraint.
