Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform

Simon M. S. LO, Stephan GESINE, Ralf A. WILKE

Research output: Journal PublicationsJournal Article (refereed)

Abstract

The copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate that the CGE imposes fewer restrictions on partial covariate effects than standard methods do. Differences are less evident when a more flexible difference-in-differences estimator is applied. It is also found that the MMPHM estimates react more strongly to the choice of the copula than the CGE in terms of the shape of the treatment effect function over time. Thus, the MMPHM produces less robust results in our application.
Original languageEnglish
JournalJournal of Econometric Methods
Volume6
Issue number1
Early online date18 Nov 2015
DOIs
Publication statusPublished - Jan 2017

Fingerprint

Unemployment duration
Copula
Competing risks
Estimator
Proportional hazards model
Treatment effects
Research methods
Cox proportional hazards model
Covariates
Difference-in-differences estimator
Applied research
Empirical research
Competing risks model

Keywords

  • Archimedean copula
  • frailty
  • policy evaluation

Cite this

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title = "Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform",
abstract = "The copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate that the CGE imposes fewer restrictions on partial covariate effects than standard methods do. Differences are less evident when a more flexible difference-in-differences estimator is applied. It is also found that the MMPHM estimates react more strongly to the choice of the copula than the CGE in terms of the shape of the treatment effect function over time. Thus, the MMPHM produces less robust results in our application.",
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Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform. / LO, Simon M. S.; GESINE, Stephan; WILKE, Ralf A.

In: Journal of Econometric Methods, Vol. 6, No. 1, 01.2017.

Research output: Journal PublicationsJournal Article (refereed)

TY - JOUR

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AU - LO, Simon M. S.

AU - GESINE, Stephan

AU - WILKE, Ralf A.

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N2 - The copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate that the CGE imposes fewer restrictions on partial covariate effects than standard methods do. Differences are less evident when a more flexible difference-in-differences estimator is applied. It is also found that the MMPHM estimates react more strongly to the choice of the copula than the CGE in terms of the shape of the treatment effect function over time. Thus, the MMPHM produces less robust results in our application.

AB - The copula graphic estimator (CGE) for competing risks models has received little attention in empirical research, despite having been developed into a comprehensive research method. In this paper, we bridge the gap between theoretical developments and applied research by considering a general class of competing risks copula models, which nests popular models such as the Cox proportional hazards model, the semiparametric multivariate mixed proportional hazards model (MMPHM), and the CGE as special cases. Analyzing the effects of a German Hartz reform on unemployment duration, we illustrate that the CGE imposes fewer restrictions on partial covariate effects than standard methods do. Differences are less evident when a more flexible difference-in-differences estimator is applied. It is also found that the MMPHM estimates react more strongly to the choice of the copula than the CGE in terms of the shape of the treatment effect function over time. Thus, the MMPHM produces less robust results in our application.

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