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).