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Job Matching on non-separated Occupational Labour Markets

TLDR
In this article, a restricted version of a Spatial Durbin Model was used to analyze the matching process in occupational labour markets in Germany, and the results showed that there are considerable dependencies between similar occupational groups in matching process.
Abstract
This work refers to analyses of matching processes on occupational labour markets in Germany. Up to now, all studies in this field are based on the crucial assumption of separate occupational labour markets. I outlined some theoretical considerations that occupational markets are probably not completely separated. By using information about similarities of occupational groups I constructed an "occupational topology" and finally tested my hypothesis of non-separated occupational labour markets with Spatial Econometric Estimators, particularly with a restricted version of a Spatial Durbin Model that includes “spatial” lags for regressors. The results show considerable dependencies between similar occupational groups in the matching process. Particularly, the results indicate occupational specific set-up and set-down processes in similar occupational groups. This has important implications for estimating the matching efficiencies of unemployed and vacancies, because the matching process is not only determined by the unemployed and vacancies in the same occupational group but also by those in other occupational groups.

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References
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Journal ArticleDOI

A new look at the statistical model identification

TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Journal ArticleDOI

Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI

Postwar U.S. Business Cycles: An Empirical Investigation

TL;DR: In this article, a procedure for representing a times series as the sum of a smoothly varying trend component and a cyclical component is proposed, and the nature of the comovements of the cyclical components of a variety of macroeconomic time series is documented.
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