On this page I put random bits of code I’ve used for a variety of projects. There is no support or guarantee that any of this code will work, so use at your own risk.

Minimization Algorithms

In many empirical projects one needs to minimize a fairly complex
criterion function. To do this I often use a kit of minimizers. I am
putting them in one place so that other researchers can have access to
them. I did not write any of this code myself.

1. FminOS

A version of a Nelson-Mead algorithm (i.e. fminsearch in MATLAB) with a
little bit of stepsize increase to get over kinks. The code in Matlab. (thanks to Robin Lee
for pointing this out).

2. Differential Evolution

I’ve found that this algorithm has a good combination of global
minimizing and relatively fast convergence for smooth problems. The code in Matlab. (thanks to a paper by Pat Bajari for suggesting this algorithm).

3. Simulated Annealing

This code takes a while to run, and I have not had much luck with it. Here is the code in MATLAB and C. (thanks to Adam Rosen for pointing this one out)

4. Numerical Recipes Minimizers

This code was written by Bo Honore in GAUSS and replicates the
minimizers in numerical recipes. The code in Gauss.

Integration

So you need to integrate over a function. Sometimes quadrature type methods work much much better than simple monte-carlo techniques.

 

1. Halton Sequences

Many economic problems have monte-carlo integration in them, but it
turns out using a uniform grid can be better than using a random
sampling procedure. The following code in MATLAB constructs Halton Sequences in N-dimensions. (thanks to Ali Yurukoglu for pointing this out).