900 research outputs found
Job search by employed workers : the effects of restrictions
No full textWithin the framework of a general equilibrium search model, the authors study the effect of institutional restrictions on workers'job mobility. The model generates endogenuous job searches on the job and off the job with two forms of labor contracts emerging and coexisting in equilibrium. One form of contract involves the workers'long-term commitment to the firm ("reversed tenure"): some firms offer high wages in return for their workers'commitment not to search for better jobs. The other is a short-term contract requiring no such commitment: some firms that cannot afford to pay wages that guarantee lifetime attachment pay lower wages, have lower turn-over costs, but impose no restrictions on searches for better jobs. The authors study the effects on employment of exogenous restrictions on mobility - in the form of a transfer from the quitting worker, made either to the employer or to a third party. These transfers, the separation bonds, are typically the benefits lost by the quitting worker, such as vested pension. Restrictions of this type, by crowding out the firms that allow on-the-job searches for employment directly increase unemployment. When restrictions on workers'mobility take the form of a zero-sum transfer, there is no real effect so long as the transfer is below some bound - the worker loses nothing. When the separation bond is prohibitively large, or when it is forfeited to a third party, employment among all types of workers falls.Health Monitoring&Evaluation,Health Economics&Finance,Environmental Economics&Policies,Economic Theory&Research,Labor Markets
Demand for Cash with Intra-Period Endogenous Consumption
No full textWe study the demand for money when agents can optimally choose mean rates of consumption and cash holdings over a period. Consistent with empirical evidence, we find that agents do not smooth intra-period consumption. Instead, their rate of consumption is positively correlated with their cash position. This positive correlation depends on the volatility of the consumption process. When volatility is very low or very high, agents choose to consume at a relatively high rate immediately after a cash withdrawal, drawing down quite rapidly their cash balances. Later in the period, their rate of consumption and cash depletion is more restrained. This sizeable deviation from consumption smoothing is much less pronounced when volatility is moderate.money demand; consumption smoothing; drift control
A Model-Based Dimension Reduction Approach to Classification of Gene Expression Data
No full textThe monitoring of the expression profiles of thousands of genes have proved to be particularly promising for biological classification, particularly for cancer diagnosis. However, microarray data present major challenges due to the complex, multiclass nature and the overwhelming number of variables characterizing gene expression profiles. We introduce a methodology that combine dimension reduction method and classification based on finite mixture of Gaussian densities. Information on the dimension reduction subspace is based on the variation of components means for each class, which in turn are obtained by modeling the within class distribution of the predictors through finite mixtures of Gaussian densities. The proposed approach is applied to the leukemia data, a well known dataset in the microarray literature. We show that the combination of dimension reduction and model-based clustering is a powerful technique to find groups among gene expression data
Closed Skew Normal Stochastic frontier Models for Panel data
No full textWe introduce a stochastic frontier model for longitudinal
data where a subject random effect coexists with a time independent
random inefficiency component and with a time dependent random
inefficiency component. The role of the closed skew normal
distribution in this kind of modeling is stressed
Archetypal Symbolic Objects
No full textSymbolic Data Analysis has represented an important
innovation in statistics since its first presentation by
E. Diday in the late 1980s. Most of the interest has
been for the statistical analysis of Symbolic Data that
represent complex data structure where variables can
assume more than just a single value. Thus, Symbolic
Data allow to describe classes of statistical units as a
whole. Furthermore, other entities can be defined in
the realm of Symbolic data. These entities are the
Symbolic objects, defined in terms of the
relationships between two different knowledge levels.
This article aims at introducing a new type of SO
based on the archetypal analysis
Using the Autodependogram in Model Diagnostic Checking
No full textIn this chapter the autodependogram is contextualized in model diagnostic checking for nonlinear models by studying the lag-dependencies of the residuals. Simulations are considered to evaluate its effectiveness in this context. An application to the Swiss Market Index is also provided
The longevity pattern in Emilia Romagna, Italy: a spatio-temporal analysis
No full textIn this paper, we investigate the pattern of longevity in an Italian region at a municipality level in two different periods. Spatio-temporal modeling is used to tackle at both periods the random variations in the occurrence of long-lived individuals, due to the rareness of such events in small areas. This method allows to exploit the spatial proximity smoothing the observed data, as well as to control for the effects of a set of regressors. As a result, clusters of areas characterized by extreme indexes of longevity are well identified and the temporal evolution of the phenomenon can be depicted. A joint analysis of male and female longevity by cohort in the two periods is conducted specifying a set of hierarchical Bayesian models
On Gaussian Compound Poisson Type Limiting Likelihood Ratio Process
No full textDifferent change-point type models encountered in statistical inference for stochastic processes give rise to different limiting likelihood ratio processes. Recently it was established that one of these likelihood ratios, which is an exponential functional of a two-sided Poisson process driven by some parameter, can be approximated (for sufficiently small values of the parameter) by another one, which is an exponential functional of a two-sided Brownian motion. In this chapter we consider yet another likelihood ratio, which is the exponent of a two- sided compound Poisson process driven by some parameter. We establish that the compound Poisson type likelihood ratio can also be approximated by the Brownian type one for sufficiently small values of the parameter. We equally discuss the asymptotics for large values of the parameter
A regionalization method for spatial functional data based on variogram models: an application on environmental data
No full text"\"This paper proposes a Dynamic Clustering Algorithm as a new regionalization. method for spatial functional data. The method looks for the best partition. optimizing a criterion of spatial association among functional data. Furthermore it. is such that a summary of the variability structure of each cluster is discovered. The. performance of the proposal is checked through an application on real data.\"
Spectral decomposition of the AR metric
No full textThis work investigates a spectral decomposition of the AR metric proposed as a measure of structural dissimilarity among ARIMA processes. Specifically, the metric will be related to the variance of a stationary process so that its behaviour in the frequency domain will help to detect how unobserved components generated by the parameters of both phenomena concur in specifying the obtained distance. Foundations for the metric are briefly reminded and the main consequences of the proposed decomposition are discussed with special reference to some specific stochastic processes in order to improve the interpretative content of the AR metric
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