Author: Wouter den Haan

The need for an approximating functional form also arises if one could
in principle calculate the function value for any given set of arguments but
that it is very expensive to do so. For example, the function value may be
the outcome of many complex calculations and it may take a lot of comput-
ing time to calculate one function value. With an approximating functional
form one could obtain (approximate) function values much quicker. Again
the goal would be to come up with an approximating functional form us-
ing a finite set of data points. There is a big difference with the problem
that the econometrician faces, however, because the econometrician can-
not choose his data points. We will see in this section that the freedom to
choose the location of the arguments makes it much easier to come up with
accurate approximations.
Finally, the theory on function approximation is very useful if one is
trying to solve for a function that is (implicitly) defined by a system of
functional equations.

Author: Wouter den Haan

The organization of this chapter is as follows. The first section covers quadrature procedures, which are the dominant way to solve models. The second section covers (pseudo) Monte Carlo integration techniques. The last section discusses quasi Monte Carlo
integration.

Author: Wouter den Haan

In this set of notes we show how perturbation techniques can be used to obtain first and higher-order Taylor expansions of the true rational expectations policy function around the steady state.

Author: Wouter den Haan

Parametrized expectations

Author: Wouter den Haan, with contributions by Joris de Wind and Ken Judd

These materials cover numerical techniques and an introduction to the popluar software Dynare. These include first-order perturbatio, higher-order pertubation, pruning and bayesian estimation in Dynare.

- Slides on Perturbation and Introduction to Dynare
- Slides on Higher-order Perturbation and Pruning
- Slides on Bayesian Estimation in Dynare

Author: Gianluca Violante

The primary goal of the course is to equip students with the numerical methods necessary to tackle interesting questions in quantitative macroeconomics. The course has two main focuses. The first is the study of computational tools and algorithms useful to solving and analyzing macro models. The second is the study of interesting applications to macroeconomics.