Quantile Regression Methods: na Application to U.S. Unemployment Duration
AbstractQuantile regression constitutes a natural and flexible framework for the analysis of duration data in general and unemployment duration in particular. Comparison of the quantile regressions for lower and upper tails of the duration distribution shed important insights on the different determinants of short or long-term unemployment. Using quantile regression techniques, we estimate conditional quantile functions of US unemployment duration; then, resampling the estimated conditional quantile process we are able to infer the implied hazard functions. The proposed methodology proves to be resilient to several misspecification that typically afflict proportional hazard models such as, neglected heterogeneity and baseline misspecification. Overall, the results provide clear indications of the interest of quantile regression to the analysis of duration data.
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Bibliographic InfoPaper provided by Banco de Portugal, Economics and Research Department in its series Working Papers with number w200201.
Date of creation: 2002
Date of revision:
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
- J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
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