The Experts below are selected from a list of 17586 Experts worldwide ranked by ideXlab platform
Joao Macieira - One of the best experts on this subject based on the ideXlab platform.
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unobserved worker quality and inter industry wage differentials
Research Papers in Economics, 2020Co-Authors: Joao MacieiraAbstract:This study quantitatively assesses two alternative explanations for inter-industry wage differentials: worker heterogeneity in the formof unobserved quality and firmheterogeneity in the form of a firm's willingness to pay (WTP) for workers' productive attributes. We develop an empirical hedonic model of labor demand and apply a two-stage Nonparametric Procedure to recover worker and firm heterogeneities. In the first stage we recover unmeasured worker quality by estimating market-specific hedonic wage functions Nonparametrically. In the second stage we infer each firm's WTP parameters for worker attributes by using first-order conditions from the demand model. We apply our approach to quantify inter-industry wage differentials on the basis of individual data from the NLSY79 and find that worker quality accounts for approximately two thirds of the inter-industry wage differentials.
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unobserved worker quality and inter industry wage differentials
Social Science Research Network, 2014Co-Authors: Joao MacieiraAbstract:This study quantitatively assesses two alternative explanations for inter-industry wage differentials: worker heterogeneity in the form of unobserved quality and firm heterogeneity in the form of the firm’s willingness to pay (WTP) for workers’ productive attributes. We develop an empirical model of labor demand and apply a two-stage Nonparametric Procedure to recover worker and firm heterogeneities. In the first stage we recover unmeasured worker quality by estimating a Nonparametric hedonic wage function. In the second stage we infer each firm’s WTP parameters for worker attributes by using first-order conditions from the demand model. We apply our approach to quantify inter-industry wage differentials on the basis of individual data from NLSY79 and find that worker quality accounts for approximately two thirds of the inter-industry wage differentials.
Victor Aguirregabiria - One of the best experts on this subject based on the ideXlab platform.
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another look at the identification of dynamic discrete decision processes an application to retirement behavior
Journal of Business & Economic Statistics, 2010Co-Authors: Victor AguirregabiriaAbstract:This article deals with the estimation of behavioral and welfare effects of counterfactual policy interventions using dynamic structural models where all the primitive functions are Nonparametrically specified (i.e., preferences, technology, transition rules, and distribution of unobserved variables). It proves the Nonparametric identification of agents’ decision rules, before and after the policy intervention, and of the change in agents’ welfare. Based on these results, I propose a Nonparametric Procedure to estimate the behavioral and welfare effects of a class of counterfactual policy interventions. The Nonparametric estimator can be used to construct a test of the validity of a parametric specification. I illustrate this method using a simple model of labor force retirement, panel data with information on public pension wealth, and a hypothetical reform that delays by three years the eligibility ages of the public pension system in Sweden.
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another look at the identification of dynamic discrete decision processes with an application to retirement behavior
2010Co-Authors: Victor AguirregabiriaAbstract:This paper deals with the estimation of the behavioral and welfare effects of counterfactual policy interventions in dynamic structural models where all the primitive functions are Nonparametrically specified (i.e., preferences, technology, transition rules, and distribution of unobserved variables). It proves the Nonparametric identification of agents' decision rules, before and after the policy intervention, and of the change in agents' welfare. Based on these results we propose a Nonparametric Procedure to estimate the behavioral and welfare effects of a general class of counterfactual policy interventions. The Nonparametric estimator can be used to construct a test of the validity of a particular parametric specification. We apply this method to evaluate hypothetical reforms in the rules of a public pension system using a model of retirement behavior and a sample of workers in Sweden.
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another look at the identification of dynamic discrete decision processes with an application to retirement behavior
2006 Meeting Papers, 2006Co-Authors: Victor AguirregabiriaAbstract:This paper presents a method to estimate the effects of a counterfactual policy intervention in the context of dynamic structural models where all the structural functions (i.e., preferences, technology, transition probabilities, and the distribution of unobservable variables) are Nonparametrically specified. We show that agents' behavior, before and after the policy intervention, and the change in agents' utility are Nonparametrically identified. Based on this result we propose a Nonparametric Procedure to estimate the behavioral and welfare effects of a general class of counterfactual policy interventions. We apply this method to evaluate hypothetical reforms in the rules of a public pension system using a model of retirement behavior and a sample of blue-collar workers in Sweden
Claudia Kirch - One of the best experts on this subject based on the ideXlab platform.
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detecting changes in the covariance structure of functional time series with application to fmri data
Econometrics and Statistics, 2020Co-Authors: Christina Stoehr, John A D Aston, Claudia KirchAbstract:Abstract Functional magnetic resonance imaging (fMRI) data provides information concerning activity in the brain and in particular the interactions between brain regions. Resting state fMRI data is widely used for inferring connectivities in the brain which are not due to external factors. As such analyzes strongly rely on stationarity, change point Procedures can be applied in order to detect possible deviations from this crucial assumption. FMRI data is modeled as functional time series and tools for the detection of deviations from covariance stationarity via change point alternatives are developed. A Nonparametric Procedure which is based on dimension reduction techniques is proposed. However, as the projection of the functional time series on a finite and rather low-dimensional subspace involves the risk of missing changes which are orthogonal to the projection space, two test statistics which take the full functional structure into account are considered. The proposed methods are compared in a simulation study and applied to more than 100 resting state fMRI data sets.
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detecting changes in the covariance structure of functional time series with application to fmri data
arXiv: Applications, 2019Co-Authors: Christina Stoehr, John A D Aston, Claudia KirchAbstract:Functional magnetic resonance imaging (fMRI) data provides information concerning activity in the brain and in particular the interactions between brain regions. Resting state fMRI data is widely used for inferring connectivities in the brain which are not due to external factors. As such analyzes strongly rely on stationarity, change point Procedures can be applied in order to detect possible deviations from this crucial assumption. In this paper, we model fMRI data as functional time series and develop tools for the detection of deviations from covariance stationarity via change point alternatives. We propose a Nonparametric Procedure which is based on dimension reduction techniques. However, as the projection of the functional time series on a finite and rather low-dimensional subspace involves the risk of missing changes which are orthogonal to the projection space, we also consider two test statistics which take the full functional structure into account. The proposed methods are compared in a simulation study and applied to more than 100 resting state fMRI data sets.
Holger Dette - One of the best experts on this subject based on the ideXlab platform.
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detection of multiple structural breaks in multivariate time series
Journal of the American Statistical Association, 2015Co-Authors: Philip Preuss, Ruprecht Puchstein, Holger DetteAbstract:We propose a new Nonparametric Procedure (referred to as MuBreD) for the detection and estimation of multiple structural breaks in the autocovariance function of a multivariate (second-order) piecewise stationary process, which also identifies the components of the series where the breaks occur. MuBreD is based on a comparison of the estimated spectral distribution on different segments of the observed time series and consists of three steps: it starts with a consistent test, which allows us to prove the existence of structural breaks at a controlled Type I error. Second, it estimates sets containing possible break points and finally these sets are reduced to identify the relevant structural breaks and corresponding components which are responsible for the changes in the autocovariance structure. In contrast to all other methods proposed in the literature, our approach does not make any parametric assumptions, is not especially designed for detecting one single change point, and addresses the problem of multiple structural breaks in the autocovariance function directly with no use of the binary segmentation algorithm. We prove that the new Procedure detects all components and the corresponding locations where structural breaks occur with probability converging to one as the sample size increases and provide data-driven rules for the selection of all regularization parameters. The results are illustrated by analyzing financial asset returns, and in a simulation study it is demonstrated that MuBreD outperforms the currently available Nonparametric methods for detecting breaks in the dependency structure of multivariate time series. Supplementary materials for this article are available online.
Dominik Wied - One of the best experts on this subject based on the ideXlab platform.
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a Nonparametric test for a constant correlation matrix
Econometric Reviews, 2017Co-Authors: Dominik WiedAbstract:We propose a Nonparametric Procedure to test for changes in correlation matrices at an unknown point in time. The new test requires constant expectations and variances, but only mild assumptions on the serial dependence structure, and has considerable power in finite samples. We derive the asymptotic distribution under the null hypothesis of no change as well as local power results and apply the test to stock returns.
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a Nonparametric test for a constant correlation matrix
arXiv: Methodology, 2012Co-Authors: Dominik WiedAbstract:We propose a Nonparametric Procedure to test for changes in correlation matrices at an unknown point in time. The new test requires only mild assumptions on the serial dependence structure and has considerable power in finite samples. We derive the asymptotic distribution under the null hypothesis of no change as well as local power results and apply the test to stock returns.
