Evaluating the Performance of the Search and Matching Model

Eran Yashiv ()

CEP Discussion Papers from Centre for Economic Performance, LSE

Abstract: Does the search and matching model fit aggregate U.S. labor market data? While the model has become an important tool of macroeconomic analysis, recent literature pointed to some failures in accounting for the data. This paper aims to answer two questions: (i) Does the model fit the data, and, if so, on what dimensions? (ii) Does the data "fit" the model, i.e. what are the data which are relevant to be explained by the model? The analysis shows that the model does fit certain specifications of the data on many dimensions, though not on all. This includes capturing the high persistence and high volatility of most of the key variables, as well as the negative co-variation of unemployment and vacancies. It offers a workable, empirically-grounded version of the model for the analysis of aggregate U.S. labor market dynamics. The paper provides macroeconomists guidance concerning the relevant "building block" for modelling the labor market, both in terms of the model and in terms of the data.

Keywords: search; matching; U.S. labor market; vacancies; labor market flows; business cycles. (search for similar items in EconPapers)
JEL-codes: E24 E32 J32 J63 (search for similar items in EconPapers)
Date: 2005-02
New Economics Papers: this item is included in nep-dge and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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https://cep.lse.ac.uk/pubs/download/dp0677.pdf (application/pdf)

Related works:
Journal Article: Evaluating the performance of the search and matching model (2006)
Working Paper: Evaluating the Performance of the Search and Matching Model (2006)
Working Paper: Evaluating the Performance of the Search and Matching Model (2005)
Working Paper: Evaluating the performance of the search and matching model (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:cepdps:dp0677

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