BatchOLS

Maximum likelihood estimation and likelihood calculations for regressions of the form

y = X⋅β + ϵ

ϵ ∼ Normal(0, v), IID

where β are the coefficients and v is the variance of the error term.

The key features are

1. type stability, also for ForwardDiff.Dual numbers,

2. fast calculations for regressions using the same X.

Example:

import BatchOLS # no exported symbols

N = 100
K = 3
X = randn(N, K)
rhs = BatchOLS.RHS(X)

## maximum likelihood estimation
for _ in 1:100
    y = randn(N)
    β, v = BatchOLS.ML_βv(y, rhs)
end

## loglikelihood calculation
β′ = randn(K)
y = randn(N)
ℓ = BatchOLS.loglikelihood(y, rhs, β′, 1.0)

The code is fairly trivial, I put it in a package to allow rigorous automated testing after optimizations, especially for type inference.