Parametric and nonparametric inference in equilibrium job search models

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.
LanguageEnglish
Pages217-244
Number of pages27
JournalAdvances in Econometrics
Volume23
DOIs
Publication statusPublished - Dec 2008

Fingerprint

Wages
Inference
Job search
Nonparametric methods
Workers
Labour market
Regularity
Model comparison
Productivity
Kernel
Bayesian methods
Leisure

Keywords

  • parametric inference
  • nonparametric inference
  • equilibrium job search models
  • econometrics

Cite this

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Parametric and nonparametric inference in equilibrium job search models. / Koop, Gary.

In: Advances in Econometrics, Vol. 23, 12.2008, p. 217-244.

Research output: Contribution to journalArticle

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