Mixed-Effects Methods for Search and Matching Research

TL;DR

This paper explores mixed-effects methods for estimating equations with person and firm effects, comparing them to traditional fixed-effects approaches and considering bias corrections in covariance estimation.

Contribution

It introduces mixed-effects methods for search and matching research, offering an alternative to fixed-effects models and addressing bias in covariance matrix estimation.

Findings

Mixed-effects methods provide a viable alternative to fixed-effects models.

Bias correction techniques improve covariance matrix estimates.

The study enhances understanding of estimation accuracy in person and firm effects.

Abstract

We study mixed-effects methods for estimating equations containing person and firm effects. In economics such models are usually estimated using fixed-effects methods. Recent enhancements to those fixed-effects methods include corrections to the bias in estimating the covariance matrix of the person and firm effects, which we also consider.