# The Informativeness of Estimation Moments

## TL;DR

This paper develops easy-to-compute measures to assess how each moment influences the precision of GMM parameter estimates, demonstrated through examples and an application to retirement planning modeling.

## Contribution

It introduces novel sensitivity measures for GMM moments that quantify their impact on estimate precision, enhancing understanding of moment importance.

## Key findings

- Measures are easy to compute and interpret.
- Application reveals moments related to retirement date differences are most informative.
- Model captures complementarities in leisure and can be applied to other contexts.

## Abstract

This paper introduces measures for how each moment contributes to the precision of parameter estimates in GMM settings. For example, one of the measures asks what would happen to the variance of the parameter estimates if a particular moment was dropped from the estimation. The measures are all easy to compute. We illustrate the usefulness of the measures through two simple examples as well as an application to a model of joint retirement planning of couples. We estimate the model using the UK-BHPS, and we find evidence of complementarities in leisure. Our sensitivity measures illustrate that the estimate of the complementarity is primarily informed by the distribution of differences in planned retirement dates. The estimated econometric model can be interpreted as a bivariate ordered choice model that allows for simultaneity. This makes the model potentially useful in other applications.