An algorithm for censored quantile regressions

In this paper, we present an algorithm for Censored Quantile Regression (CQR) estimation problems. Our method permits CQR estimation problems to be solved more efficiently and reliably than was hitherto possible. It guarantees to find a high quality estimator in O(k×n²) operations with k regressors and n observations, which is much less than the existing algorithms for CQR problems.Cencored Quantile Regression

Using Quantile Regression for Duration Analysis

Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression address the issue of right censoring of the response variable which is common in duration analysis. We compare quantile regression to standard duration models. Quantile regression do not impose a proportional effect of the covariates on the hazard over the duration time. However, the method can not take account of time{varying covariates and it has not been extended so far to allow for unobserved heterogeneity and competing risks. We also discuss how hazard rates can be estimated using quantile regression methods. A small application with German register data on unemployment duration for younger workers demonstrates the applicability and the usefulness of quantile regression for empirical duration analysis. --censored quantile regression,unemployment duration,unobserved heterogeneity,hazard rate

Inference on Counterfactual Distributions

Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States. As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the \textit{entire} conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals.Comment: 55 pages, 1 table, 3 figures, supplementary appendix with additional results available from the authors' web site

Genetic algorithms: a tool for optimization in econometrics - basic concept and an example for empirical applications

This paper discusses a tool for optimization of econometric models based on genetic algorithms. First, we briefly describe the concept of this optimization technique. Then, we explain the design of a specifically developed algorithm and apply it to a difficult econometric problem, the semiparametric estimation of a censored regression model. We carry out some Monte Carlo simulations and compare the genetic algorithm with another technique, the iterative linear programming algorithm, to run the censored least absolute deviation estimator. It turns out that both algorithms lead to similar results in this case, but that the proposed method is computationally more stable than its competitor. --Genetic Algorithm,Semiparametrics,Monte Carlo Simulation

Censored Quantile Regression Redux

Quantile regression for censored survival (duration) data offers a more flexible alternative to the Cox proportional hazard model for some applications. We describe three estimation methods for such applications that have been recently incorporated into the R package quantreg: the Powell (1986) estimator for fixed censoring, and two methods for random censoring, one introduced by Portnoy (2003), and the other by Peng and Huang (2008). The Portnoy and Peng-Huang estimators can be viewed, respectively, as generalizations to regression of the Kaplan-Meier and Nelson-Aalen estimators of univariate quantiles for censored observations. Some asymptotic and simulation comparisons are made to highlight advantages and disadvantages of the three methods.

Seniority and Job Stability: A Quantile Regression Approach Using Matched Employer-Employee Data

Job mobility and employment durations can be explained by different theoretical approaches, such as job matching or human capital theory or dual labor market approaches. These models may, however, apply to different degrees at different durations in the employment spell. Standard empirical techniques, such as hazard rate analysis, cannot deal with this problem. In this paper, we apply censored quantile regression techniques to estimate employment durations of male workers in Germany. Our results give some support to the job matching model: individuals with a high risk of being bad matches exhibit higher exit rates initially, but the effect fades out over time. By contrast, the influence of human capital variables such as education and further training decreases with employment duration, which is inconsistent with the notion of increasing match-specific rents due to human capital accumulation. The results also suggest that the effects of certain labor market institutions, such as works councils, differ markedly between short-term and long-term employment, supporting the view that institutions give rise to dual labor markets. --Job Durations,Mobility,Matching,Human Capital,Quantile Regression

U.S. Unemployment Duration: Has Long Become Longer or Short Become Shorter?

The U.S. labor market has been experiencing unprecedented high average unemployment duration. The shift in the unemployment duration distribution can be traced back to the early nineties. In this study, censored quantile regression methods are employed to analyze the changes in the US unemployment duration distribution. We explore the decomposition method proposed by Machado and Mata (2005) to disentangle the contribution of the changes generated by the covariate distribution and by the conditional distribution. The data used in this inquiry are taken from the nationally representative Displaced Worker Surveys of 1988 and 1998. We provide evidence that the change in the unemployment duration distribution is mainly produced by the opposing effects of a sharp rise in job-to-job transition rates and an increased sensitivity of unemployment duration to unemployment rates. Compositional changes in the labor force played a limited role. We rationalize our findings by arguing that improved screening technology is likely to be the relevant underlying mechanism at work.

Job seeker's allowance in Great Britain: How does the regional labour market affect the duration until job finding?

Employing a large individual-level administrative dataset from Great Britain, covering the period 1999-2007, we analyse the factors influencing the length of unemployment benefits claimant periods with subsequent transition to re-employment. To this end, this individual-level data is merged with a group of regional indicators to control for relevant regional labour market characteristics. From a methodological point of view, we adopt a flexible censored quantile regression approach to estimating conditional re-employment hazards. Our results indicate that the individual characteristics of an unemployed person are generally more im- portant than the regional labour market conditions. However, regional labour supply and demand conditions are important determinants for the length of unemployment compensation claim periods. Our analysis provides evidence that large cities such as London and Birmingham provide the worse local labour market conditions for job seekers allowance recipients, while remote regions like the Shetland islands perform among the best.benefit duration, quantile regression, hazard rate.

Beyond the mean gender wage gap : decomposition of differences in wage distributions using quantile regression

Using linked employer-employee data, this study measures and decomposes the differences in the earnings distribution between male and female employees in Germany. I extend the traditional decomposition to disentangle the effect of human capital characteristics and the effect of firm characteristics in explaining the gender wage gap. Furthermore, I implement the decomposition across the whole wage distribution with the method proposed by Machado and Mata (2005). Thereby, I take into account the dependence between the human capital endowment of individuals and workplace characteristics. The selection of women into less successful and productive firms explains a sizeable part of the gap. This selection is more pronounced in the lower part of the wage distribution than in the upper tail. In addition, women also benefit from the success of firms by rent-sharing to a lesser extent than their male colleagues. This is the source of the largest part of the pay gap. Gender differences in human capital endowment as well s differences in returns to human capital are less responsible for the wage differential

Using Quantile Regression for Duration Analysis

Quantile regression methods are emerging as a popular technique in econometrics and biometrics for exploring the distribution of duration data. This paper discusses quantile regression for duration analysis allowing for a flexible specification of the functional relationship and of the error distribution. Censored quantile regression address the issue of right censoring of the response variable which is common in duration analysis. We compare quantile regression to standard duration models. Quantile regression do not impose a proportional effect of the covariates on the hazard over the duration time. However, the method can not take account of time{varying covariates and it has not been extended so far to allow for unobserved heterogeneity and competing risks. We also discuss how hazard rates can be estimated using quantile regression methods. A small application with German register data on unemployment duration for younger workers demonstrates the applicability and the usefulness of quantile regression for empirical duration analysis