Simple Non-parametric Estimators for Unemployment Duration Analysis

Laura Wichert; Ralf Wilke

doi:10.1111/j.1467-9876.2007.00604.x

Simple Non-parametric Estimators for Unemployment Duration Analysis

Laura Wichert, Ralf Wilke

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

We consider an extension of conventional univariate Kaplan–Meier-type estimators for the hazard rate and the survivor function to multivariate censored data with a censored random regressor. It is an Akritas-type estimator which adapts the non-parametric conditional hazard rate estimator of Beran to more typical data situations in applied analysis. We show with simulations that the estimator has nice finite sample properties and our implementation appears to be fast. As an application we estimate non-parametric conditional quantile functions with German administrative unemployment duration data.

Original language	English
Journal	Journal of the Royal Statistical Society, Series C (Applied Statistics)
Volume	27
Issue number	1
Pages (from-to)	117–126
ISSN	0035-9254
DOIs	https://doi.org/10.1111/j.1467-9876.2007.00604.x
Publication status	Published - 2008
Externally published	Yes

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Cite this

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title = "Simple Non-parametric Estimators for Unemployment Duration Analysis",

abstract = "We consider an extension of conventional univariate Kaplan–Meier-type estimators for the hazard rate and the survivor function to multivariate censored data with a censored random regressor. It is an Akritas-type estimator which adapts the non-parametric conditional hazard rate estimator of Beran to more typical data situations in applied analysis. We show with simulations that the estimator has nice finite sample properties and our implementation appears to be fast. As an application we estimate non-parametric conditional quantile functions with German administrative unemployment duration data.",

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AU - Wilke, Ralf

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N2 - We consider an extension of conventional univariate Kaplan–Meier-type estimators for the hazard rate and the survivor function to multivariate censored data with a censored random regressor. It is an Akritas-type estimator which adapts the non-parametric conditional hazard rate estimator of Beran to more typical data situations in applied analysis. We show with simulations that the estimator has nice finite sample properties and our implementation appears to be fast. As an application we estimate non-parametric conditional quantile functions with German administrative unemployment duration data.

AB - We consider an extension of conventional univariate Kaplan–Meier-type estimators for the hazard rate and the survivor function to multivariate censored data with a censored random regressor. It is an Akritas-type estimator which adapts the non-parametric conditional hazard rate estimator of Beran to more typical data situations in applied analysis. We show with simulations that the estimator has nice finite sample properties and our implementation appears to be fast. As an application we estimate non-parametric conditional quantile functions with German administrative unemployment duration data.

KW - Censoring

KW - Non-parametric estimation

KW - Unemployment duration

U2 - 10.1111/j.1467-9876.2007.00604.x

DO - 10.1111/j.1467-9876.2007.00604.x

M3 - Journal article

SN - 0035-9254

VL - 27

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EP - 126

JO - Journal of the Royal Statistical Society, Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society, Series C (Applied Statistics)

IS - 1

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