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Bayesian inference in models based on equilibrium search theory

机译:基于均衡搜索理论的模型贝叶斯推断

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摘要

The equilibrium search model represents a substantial advance over previous models of job search, but its empirical performance fails in some crucial ways. Hence, flexible extensions are called for. In this paper, we consider three such extensions inareas where Bayesian methods can be used to great advantage. These are: (i) a specification where Bayesian priors are used to center a model over the restrictions implied by economic theory; (ii) a model involving a nonlinear production function where aclosed form expression for the likelihood function does not exist; and (iii) a model which allows for unobserved heterogeneity. We show how posterior simulation methods can be used for empirical analysis for all three models. The paper includes an empirical exercise involving the school-to-work transitions of Canadian and US school leavers.
机译:均衡搜索模型比以前的求职模型具有实质性的进步,但是其经验表现在某些关键方面失败了。因此,需要灵活的扩展。在本文中,我们考虑了三个此类扩展区域,在这些区域中可以使用贝叶斯方法来发挥极大的优势。这些是:(i)使用贝叶斯先验将模型置于经济理论所隐含的限制之上的规范; (ii)包含非线性生产函数的模型,其中不存在似然函数的闭式表达式; (iii)允许未观察到的异质性的模型。我们展示了后仿真方法如何用于所有三个模型的经验分析。本文包括一项涉及加拿大和美国离校生从学校到工作过渡的实证研究。

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