# Sandwich estimators for standard errors

## Other FAQs about standard errors

- Where is the model fitting information stored in MLwiN?
- Should the comparative SD output when I calculate the residuals be different for each row?
- MLwiN is giving the standard errors of parameter estimates as 0, but I know from comparison with other software packages that the standard errors should not be 0

## Can MLwiN produce robust standard errors?

Sandwich estimators for standard errors are often useful, eg when model based estimators are very complex and difficult to compute and robust alternatives are required.

Consider the fixed part parameter estimates

The covariance matrix is given by

If we replace the central covariance term by the usual (Normal) model based value, *V*, we obtain the usual formula

with sample estimates being substituted. The sandwich estimator is formed by replacing the estimate of the central covariance term, , by an empirical estimator based on the (block diagonal structure) cross product matrix, namely

## Residuals

For residuals the estimated set of residuals for the *j-*th block at level *h*, using a similar notation to Goldstein (1995, App. 2.2) omitting the sub/superscript *h*, is given by

To obtain consistent estimators of the covariance matrix of these residuals (ignoring variation in the fixed parameter estimates) we can choose *comparative* or *diagnostic* estimators.

The diagnostic estimator is given by

If the model based estimator is used this reduces to the expression given by Goldstein (1995, Appendix 2.2), otherwise the cross product matrix estimator is used.

For the comparative estimator we have

which reduces to the expression in Goldstein (1995, Appendix 2.2) when the model based estimator is used.

In MLwiN 1.1 access to the sandwich estimators is via the FSDE and RSDE commands

For residuals, sandwich estimators will automatically be used when weighted residuals are specified in the residuals section on weighting for details of residuals produced from weighted models.