To move or not to move to find a new job: spatial duration time model with dynamic covariate effects

The aim of this paper is to show the flexibility and capacity of penalized spline smoothing as estimation routine for modelling duration time data. We analyse the unemployment behaviour in Germany between 2000 and 2004 using a massive database from the German Federal Employment Agency. To investigate dynamic covariate effects and differences between competing job markets depending on the distance between former and recent working place, a functional duration time model with competing risks is used. It is build upon a competing hazard function where some of the smooth covariate effects are allowed to vary with unemployment duration. The focus of our analysis is on contrasting the spatial, economic and individual covariate effects of the competing job markets and on analysing their general influence on the unemployed's re-employment probabilities. As a result of our analyses, we reveal differences concerning gender, age and education. We also discover an effect between the newly formed and the old West German states. Moreover, the spatial pattern between the considered job markets differs.     

Reemployment Probability and Multiple Unemployment Spells: A Partial-Likelihood Approach

An extended version of the mixed-proportional-hazards model is used to study the determinants of the conditional probability of reemployment of long-term unemployed young persons sampled in the first stage of the Australian National Longitudinal Survey. The survey data facilitate an econometric comparison between the effect of different factors on the conditional reemployment probability of those in their first unemployment spell with those who have experienced multiple spells. The article uses Cox's partial-likelihood approach. The empirical results support the hypothesis of lagged-duration dependence in that the length of previous job experience or the duration of previous unemployment is found to be an important determinant of reemployment probability. The results strongly suggest that fitting a common duration model to data from different spells involves a major misspecification.     

Monte Carlo integration: An application to labour market states and transitions

This paper is concerned with the application of simulation estimation methods to micro-econometric labour market models. Based on a multi-period probit model for direct job changes and unemployment, estimators for the likelihood of individual employment histories are obtained by Monte Carlo integration and employed in a standard ML-procedure. The results for West German panel data suggest that dynamic effects are largely prevalent on labour markets and that in particular, past unemployment has drastic negative effects on future employment chances. Further, there are no indications that foreigners have a different labour market performance, nor that they are crowding natives out into unemployment.     

A spatial analysis on Italian unemployment differences

Using spatial econometric models, this paper focuses attention on the spatial structure of provincial unemployment disparities of Italian provinces for the year 2003. On the basis of findings from the economic literature and of the available socio-economic data, various model specifications including supply- and demand-side variables are tested. Further we use ESDA analysis as equivalent to integration analysis on time series; therefore it is applied on each variable, dependent and independent, involved in the statistical model. The suggestions of ESDA lead us to the most adequate statistical model, which estimates indicate that there is a significant degree of neighbouring effect (i.e. positive spatial correlation) among labour markets at the provincial level in Italy; this effect is present notwithstanding we controlled for local characteristics. The unemployment shows a polarized spatial pattern that is strongly connected to labour demand and to a much lesser extent to the share of young population and economic structural composition.     

衡量劳动市场供求状况的另一个指标：求人倍率

本文详细整理分析了我国政府分别公布的劳动力市场和人才市场的两套求人倍率统计的特点,分析了求人倍率与失业率的区别,比较了我国的求人倍率统计与世界各国求人倍率统计的异同点。根据我国求人倍率的数据分析了我国劳动市场的供求状况,其中一个重要特征是低学历劳动者的供求压力比高学历劳动者小得多。最后给出了我国求人倍率的详细的明确的定义,提出了今后完善这项统计时的注意事项。     

Survey non-response and the duration of unemployment

Summary.  Social surveys are often used to estimate unemployment duration distributions. Survey non-response may then cause a bias. We study this by using a data set that combines survey information of individual workers with administrative records of the same workers. The latter provide information on durations of unemployment and personal characteristics of all survey respondents and non-respondents. We develop a method to distinguish empirically between two explanations for a bias in results based on only survey data: selectivity due to related unobserved determinants of durations of unemployment and non-response and a causal effect of a job exit on non-response. The latter may occur even in fully homogeneous populations. The methodology exploits variation in the timing of the duration outcome relative to the survey moment. The results show evidence for both explanations. We discuss implications for standard methods to deal with non-response bias.     

Marginal information for structure learning

Structure learning for Bayesian networks has been made in a heuristic mode in search of an optimal model to avoid an explosive computational burden. In the learning process, a structural error which occurred at a point of learning may deteriorate its subsequent learning. We proposed a remedial approach to this error-for-error process by using marginal model structures. The remedy is made by fixing local errors in structure in reference to the marginal structures. In this sense, we call the remedy a marginally corrective procedure. We devised a new score function for the procedure which consists of two components, the likelihood function of a model and a discrepancy measure in marginal structures. The proposed method compares favourably with a couple of the most popular algorithms as shown in experiments with benchmark data sets.     

基于行政大数据的失业率估计:以某四百万人口城市为例

我国城镇登记失业率指标稳定在4%左右,难以较为准确反映就业动态;而劳动力调查样本量有限,城镇调查失业率对省以下各级行政区域代表性不足。本文将针对大数据的机器学习算法与针对传统统计数据的核算思想结合起来,基于某四百万人口城市2016—2018年的全样本行政大数据,利用机器学习算法,对每个城镇居民每个月的就业状态进行预测,再利用统计核算方法,估计出该城市的失业率。在个人层面,本文的模型在样本外测试集上的准确率达到96.7%。经过统计核算加总,本文估计的当地失业率在合理区间范围内,并表现出明显的周期性特征,对就业形势动态变化的刻画明显优于当地一年发布一次的登记失业率数据。本文基于个人层面的预测结果,进一步探讨了当地失业人口 的性别与文化程度特征,以及再就业的时间规律。本文针对如何使用行政大数据辅助经济决策提出了新的范式,对大数据时代如何理解经济与制定政策具有参考意义。     

A gradient approach to efficient design and analysis of multivariate EWMA control charts

Compared to the grid search approach to optimal design of control charts, the gradient-based approach is more computationally efficient as the gradient information indicates the direction to search the optimal design parameters. However, the optimal parameters of multivariate exponentially weighted moving average (MEWMA) control charts are often obtained by using grid search in the existing literature. Note that the average run length (ARL) performance of the MEWMA chart can be calculated based on a Markov chain model, making it feasible to estimate the ARL gradient from it. Motivated by this, this paper develops an ARL gradient-based approach for the optimal design and sensitivity analysis of MEWMA control charts. It is shown that the proposed method is able to provide a fast, accurate, and easy-to-implement algorithm for the design and analysis of MEWMA charts, as compared to the conventional design approach based on grid search.     

Hierarchical Bayes estimation of spatial statistics for rates

The U.S. Bureau of Labour Statistics publishes monthly unemployment rate estimates for its 50 states, the District of Columbia, and all counties, under Current Population Survey. However, the unemployment rate estimates for some states are unreliable due to low sample sizes in these states. Datta et al. (1999) proposed a hierarchical Bayes (HB) method using a time series generalization of a widely used cross-sectional model in small-area estimation. However, the geographical variation is also likely to be important. To have an efficient model, a comprehensive mixed normal model that accounts for the spatial and temporal effects is considered. A HB approach using Markov chain Monte Carlo is used for the analysis of the U.S. state-level unemployment rate estimates for January 2004-December 2007. The sensitivity of such type of analysis to prior assumptions in the Gaussian context is also studied.     

Bias-variance trade-off for prequential model list selection

The prequential approach to statistics leads naturally to model list selection because the sequential reformulation of the problem is a guided search over model lists drawn from a model space. That is, continually updating the action space of a decision problem to achieve optimal prediction forces the collection of models under consideration to grow neither too fast nor too slow to avoid excess variance and excess bias, respectively. At the same time, the goal of good predictive performance forces the search over good predictors formed from a model list to close in on the data generator. Taken together, prequential model list re-selection favors model lists which provide an effective approximation to the data generator but do so by making the approximation match the unknown function on important regions as determined by empirical bias and variance.     

Bayesian Analysis of the Prototypal Search Model

Bayesian analysis for a simple but widely applied dynamic programming model is obtained. The setting is the prototypal job-search model. The general case of wage and duration data, with potential censoring, is studied. The optimality condition implied by the dynamic programming setup is fully imposed. The posterior distribution reveals a “ridge” reflecting the characteristic nonstandard nature of the inference problem. Marginal distributions and moments are obtained in a canonical parameterization after a suitable approximation. The adequacy of the approximation is easily assessed. Simulation is applied to study alternative parameterizations and prior robustness and to facilitate prior elicitations. Finally, we illustrate the applicability of our methods by giving posterior distributions for the elasticities of unemployment durations and reemployment wages with respect to unemployment income. Our analysis is easy to implement and all computations are simple to perform.     

Robust Estimation of a Spatiotemporal Model with Structural Change

A spatiotemporal model is postulated and estimated using a procedure that infuses the forward search algorithm and maximum likelihood estimation into the backfitting framework. The forward search algorithm filters the effect of temporary structural change in the estimation of covariate and spatial parameters. Simulation studies illustrate capability of the method in producing robust estimates of the parameters even in the presence of structural change. The method provides good model fit even for small sample sizes in short time series data and good predictions for a wide range of lengths of contamination periods and levels of severity of contamination.     

Semi-parametric Bayesian Analysis of the Proportional Hazard Rate Model An Application to the Effect of Training Programs on Graduate Unemployment

In this paper, we introduce a semi-parametric Bayesian methodology based on the proportional hazard model that assumes that the baseline hazard function is constant over segments but, by contrast to what is usually assumed in the literature, with the periods at which the function changes not being specified in advance. The methodology is applied to explore the impact of Vocational Training courses offered by the University of Zaragoza (Spain) on the duration of the initial periods of unemployment experienced by graduate leavers. The framework is very flexible and allows us, in particular, to capture the presence of seasonality in the job insertion of graduates.     

Time Series Evidence of Unemployment Flows: The Sample Period Matters

Worker flows to and from unemployment occur at all stages of the U.S. business cycle. The flows' dynamics affect the dynamics of the unemployment rate. This article studies the dynamics of unemployment flows and the unemployment rate and explores and documents changes over time. Rigorous diagnostic tests are used to investigate the structural stability of projectional relationships involving flows and the unemployment rate.     

Uneven two-sided power distributions with applications in econometric models

In this paper we propose and analyze a bounded density function with a jump discontinuity at a threshold. Its properties are presented and a maximum likelihood estimation (MLE) procedure for the threshold location and jump size is developed. The distribution seems be appropriate in the context of financial engineering, production analysis, standard auction models and the equilibrium job search problem. An example of the MLE procedure is given utilizing an i.i.d. sample of standardized log differences of bi-monthly US Certificate Deposit interest rates for the period from 1966-2002. The corresponding time series was constructed using an Auto-Regressive Conditional Heteroscedastic (ARCH) model.     

Minimizing the expected time to detect a randomly located lost target using 3-dimensional search technique

Abstract

This paper considers a new model in search theory to find a randomly located target in the 3-dimensional space. An approximation algorithm that facilitates searching procedures for searchers or robots is presented. The expected time to detect the target is also proved. The statistical analysis by calculating the optimal search strategy which minimizes the time to detect the target, assuming trivariate standard normal distribution is provided, and the technique by flowcharts is designed as well. The effectiveness of this strategy is illustrated by introducing an application from real world.     

Nonparametric Inference for Time-Varying Coefficient Quantile Regression

The article considers nonparametric inference for quantile regression models with time-varying coefficients. The errors and covariates of the regression are assumed to belong to a general class of locally stationary processes and are allowed to be cross-dependent. Simultaneous confidence tubes (SCTs) and integrated squared difference tests (ISDTs) are proposed for simultaneous nonparametric inference of the latter models with asymptotically correct coverage probabilities and Type I error rates. Our methodologies are shown to possess certain asymptotically optimal properties. Furthermore, we propose an information criterion that performs consistent model selection for nonparametric quantile regression models of nonstationary time series. For implementation, a wild bootstrap procedure is proposed, which is shown to be robust to the dependent and nonstationary data structure. Our method is applied to studying the asymmetric and time-varying dynamic structures of the U.S. unemployment rate since the 1940s. Supplementary materials for this article are available online.     

Measuring the Influence of Unemployment Insurance on Unemployment Experiences

This article investigates the comprehensive effects of unemployment insurance (UI) policies on the amount of time and unemployment that individuals report between jobs. The econometric model jointly determines the effects of UI on the lengths of nonemployment spells, the classification of these spells as unemployment, and the likelihood of collecting program benefits. The model carefully attempts to isolate variation in UI benefits attributable to differences in generosity across programs to avoid biases in estimating policy effects induced by other contaminating sources of benefit variation. Using data on men from the National Longitudinal Survey of Youth, the empirical results find (a) UI recipients typically experience longer spells between jobs, at least up to the exhaustion of UI benefits, and report substantially larger fractions of these spells as unemployment; (b) weekly benefit amounts exert no significant influence on the likelihood of UI recipiency, on the length of spells between jobs, or on the fraction of these spells classified as unemployment; and (c) increases in weeks of UI eligibility raise the likelihood of UI collection and lengthen the number of weeks of unemployment between jobs by inducing long spells to become longer and not by altering short-duration behavior.     

Considering Taguchi loss function on statistically constrained economic sum of squares exponentially weighted moving average charts

In this paper, Duncan's cost model combined Taguchi's quadratic loss function is applied to develop the economic-statistical design of the sum of squares exponentially weighted moving average (SS-EWMA) chart. The genetic algorithm is applied to search for the optimal decision variables of SS-EWMA chart such that the expected cost is minimized. Sensitivity analysis reveals that the optimal sample size and sampling interval decrease; optimal smoothing constant and control limit increase as the mean and/or variance increases. Moreover, the combination of optimal parameter levels in orthogonal array experiment plays an important guideline for monitoring the process mean and/or variance.