Simple non-parametric estimators for unemployment duration analysis

Authors: Wichert, Laura¹; Wilke, Ralf A.²

Source: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 57, Number 1, February 2008 , pp. 117-126(10)

Publisher: Wiley-Blackwell

Buy & download fulltext article:

Price: $48.00 plus tax (Refund Policy)

Abstract:

Summary.

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.

Keywords: Censoring; Non-parametric estimation; Unemployment duration

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9876.2008.00604.x

Affiliations: 1: University of Konstanz, Germany 2: University of Nottingham, UK

Publication date: 2008-02-01