Unemployment Duration Data Research Articles

9. Dynamic Discrete-Time Duration Models: Estimation Viamarkov Chain Monte Carlo

Discrete-time or grouped duration data, with one or multiple types of terminating events, are often observed in social sciences or economics. In this paper we suggest and discuss dynamic models for flexible Bayesian nonparametric analysis of such data. These models allow simultaneous incorporation and estimation of baseline hazards and time-varying covariate effects, with out imposing particular parametric forms. Methods for exploring the possibility of time-varying effects, as for example the impact of nationality or unemployment insurance benefits on the probability of reemployment, have recently gained increasing interest. Our modeling and estimation approach is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques. A detailed analysis of unemployment duration data, with full-time job, part-time job and other causes as terminating events, illustrates our methods and shows how they can be used to obtain refined results and interpretations.

Smoothing Hazard Functions and Time-Varying Effects in Discrete Duration and Competing Risks Models

Abstract State-space or dynamic approaches to discrete or grouped duration data with competing risks or multiple terminating events allow simultaneous modeling and smooth estimation of hazard functions and time-varying effects in a flexible way. Full Bayesian or posterior mean estimation, using numerical integration techniques or Monte Carlo methods, can become computationally rather demanding or even infeasible for higher dimensions and larger datasets. Therefore, based on previous work on filtering and smoothing for multicategorical time series and longitudinal data, our approach uses posterior mode estimation. Thus we have to maximize posterior densities or, equivalently, a penalized likelihood, which enforces smoothness of hazard functions and time-varying effects by a roughness penalty. Dropping the Bayesian smoothness prior and adopting a nonparametric viewpoint, one might also start directly from maximizing this penalized likelihood. We show how Fisher scoring smoothing iterations can be carried out efficiently by iteratively applying linear Kalman filtering and smoothing to a working model. This algorithm can be combined with an EM-type procedure to estimate unknown smoothing parameters or hyperparameters. The methods are applied to a larger set of unemployment duration data with one terminating event and, in a further analysis, multiple terminating events from the German socioeconomic panel GSOEP.

Smoothing Hazard Functions and Time-Varying Effects in Discrete Duration and Competing Risks Models

Abstract State-space or dynamic approaches to discrete or grouped duration data with competing risks or multiple terminating events allow simultaneous modeling and smooth estimation of hazard functions and time-varying effects in a flexible way. Full Bayesian or posterior mean estimation, using numerical integration techniques or Monte Carlo methods, can become computationally rather demanding or even infeasible for higher dimensions and larger datasets. Therefore, based on previous work on filtering and smoothing for multicategorical time series and longitudinal data, our approach uses posterior mode estimation. Thus we have to maximize posterior densities or, equivalently, a penalized likelihood, which enforces smoothness of hazard functions and time-varying effects by a roughness penalty. Dropping the Bayesian smoothness prior and adopting a nonparametric viewpoint, one might also start directly from maximizing this penalized likelihood. We show how Fisher scoring smoothing iterations can be carried out efficiently by iteratively applying linear Kalman filtering and smoothing to a working model. This algorithm can be combined with an EM-type procedure to estimate unknown smoothing parameters or hyperparameters. The methods are applied to a larger set of unemployment duration data with one terminating event and, in a further analysis, multiple terminating events from the German socioeconomic panel GSOEP.

Digit preference in CPS unemployment data

In analyses of CPS unemployment duration data it is necessary to ‘smooth’ the observed responses, prior to inference, to accommodate the effects of digit preference. In this note I show that popular methods of smoothing have significant effects on estimates of expected duration; the time series properties of this statistic are less severely affected.

Flexible parametric estimation of duration and competing risk models

AbstractIn this paper we specify and estimate a flexible parametric proportional hazards model. The model specification is flexibly parametric in the sense that the baseline hazard is non parametric while the effect of the covariates takes a particular functional form. We also add parametric heterogeneity to the underlying hazard model specification. We specify a flexible parametric proportional competing risks model which permits unrestricted correlation among the risks. Unemployment duration data are then analysed using the flexible parametric duration and competing risks specifications. We find an important effect arising from the exhaustion of unemployment insurance and significantly different hazards for the two types of risks, new jobs and recalls.

Some Generalized Mixture Distributions with an Application to Unemployment Duration

Compounding or mixture distributions provide a rich class of models for applications ranging from models of heterogeniety, measurement error, distribution of stock returns and income to models of unemployment duration. Some very general mixtures are considered which include many new mixture models and also provide a unified method of organizing and comparing previously considered models as well as a test of heterogeneity. These models are used to analyze CPS unemployment duration data. A heterogeniety interpretation of the mixture models explains the discrepancy between implications of search theory and patterns observed in aggregate unemployment data. Copyright 1987 by MIT Press.

Trends in Unemployment Duration Data

A method of estimating the distribution of uninterrupted unemploymentspells from aggregate unemployment data is outlined. The methodology does not i mpose constant individual hazard rates and allows for heterogeneity. Characteris tics of the distribution of unemployment spells are then considered including ha zard rates, measures of inequality such as Gini coefficients, and the fraction o f total unemployment associated with S weeks of unemploy-ment. The relationship between these char-acteristics and labor demand variables (GNP) and supply-side variables (transfer payments, unemployment insurance) is considered in or-der to better understand the relative role of short- and long-run unemployment. Copyright 1986 by MIT Press.

Unemployment insurance eligibility rights: Evidence from a comparison of Australia and Canada

The Australian unemployment assistance scheme possesses two major features which differentiate it from North American programs. First, no qualifying period of employment is necessary. Second, benefits may be paid for an unlimited period. Analysis of the individual's work-leisure choice provides a number of predictions of the likely impact of these arrangements upon the probability of being in various labormarket states. These predictions are tested by comparing Australian unemployment flow and duration data with data for Canada.