Parametric and nonparametric inference in equilibrium job search models

Koop, Gary (2008) Parametric and nonparametric inference in equilibrium job search models. Advances in Econometrics, 23. pp. 217-244. ISSN 0731-9053 (https://doi.org/10.1016/S0731-9053(08)23007-0)

[thumbnail of strathprints007753]
Preview
 Text. Filename: strathprints007753.pdf
Accepted Author Manuscript
Download (294kB)| Preview

Abstract

Equilibrium job search models allow for labor markets with homogeneous workers and firms to yield nondegenerate wage densities. However, the resulting wage densities do not accord well with empirical regularities. Accordingly, many extensions to the basic equilibrium search model have been considered (e.g., heterogeneity in productivity, heterogeneity in the value of leisure, etc.). It is increasingly common to use nonparametric forms for these extensions and, hence, researchers can obtain a perfect fit (in a kernel smoothed sense) between theoretical and empirical wage densities. This makes it difficult to carry out model comparison of different model extensions. In this paper, we first develop Bayesian parametric and nonparametric methods which are comparable to the existing non-Bayesian literature. We then show how Bayesian methods can be used to compare various nonparametric equilibrium search models in a statistically rigorous sense.

ORCID iDs

Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X;
CORE (COnnecting REpositories)