# Generalised Least Squares

## Generalised Least Squares (GLS)

### The Generalised Least Squares (GLS) estimator

#### Introduction

We make the same assumptions as OLS.

\(\mathbf {y}=\mathbf {X}\theta+\boldsymbol {\epsilon }\)

We assume:

#### The GLS estimator

GLS estimator is:

\(\hat \theta_{GLS} = argmin_b (y-Xb)^T\Omega^{-1}(y-Xb)\)

\(\hat \theta_{GLS}=(X^T\Omega ^{-1}X)^{-1}X^T\Omega^{-1}y\)

This is the vector that minimises the Mahalanobis distance.

This is equivalent to doing OLS on a linearly transformed version of the data.

#### Identifying \(\Omega \)

If \(\Omega \) is known, we can proceed. Generally, however, \(\Omega \) is not known, and so the GLS estimate in infeasible.

## Feasible Generalised Least Squares (FGLS)

### The Feasible Generalised Least Squares (FGLS) estimator

#### Introduction

We do OLS to get a consistent estimate of \(\Omega \), \(\hat \Omega \).

We then plug this into the GLS estimator.

## Heteroskedasticity

### Weighted least squares

## Bias and variance of the GLS estimator

### Introduction

you have the same sandwich term as before, so same process, right?

## Linear discriminant analysis