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Several modern textbooks report that, thanks to the availability of heteroskedasticity robust standard errors, one observes the near-death of Weighted Least Squares (WLS) in cross-sectional applied work. We argue in this paper that it is actually possible to estimate regression parameters at least as precisely as Ordinary Least Squares (OLS) and WLS, even when using a misspecified parametric model for conditional heteroskedasticity. Our analysis is valid for a general regression framework (including Instrumental Variables and Nonlinear Regression) as long as the regression is defined by a conditional expectation condition. The key is to acknowledge, as first pointed out by Cragg (1992) that, when the user-specific heteroskedasticity model is misspecified, WLS has to be modified depending on a choice of some univariate target for estimation. Moreover, targeted WLS can be improved by properly combining moment equations for OLS and WLS respectively. Efficient GMM must be regularized to take into account the possible multicollinearity of estimating equations when errors terms are actually nearly homoscedastic.

One of the most important properties influencing the chemical behavior of an element is the electron affinity (EA). Among the remaining elements with unknown EA is astatine, where one of its isotopes, At-211, is remarkably well suited for targeted radionuclide therapy of cancer. With the At- anion being involved in many aspects of current astatine labeling protocols, the knowledge of the electron affinity of this element is of prime importance. Here we report the measured value of the EA of astatine to be 2.41578(7)eV. This result is compared to state-of-the-art relativistic quantum mechanical calculations that incorporate both the Breit and the quantum electrodynamics (QED) corrections and the electron-electron correlation effects on the highest level that can be currently achieved for many-electron systems. The developed technique of laser-photodetachment spectroscopy of radioisotopes opens the path for future EA measurements of other radioelements such as polonium, and eventually super-heavy elements. Electron affinity (EA) is a key parameter in determining the chemical behavior of the elements, but challenging to measure for unstable atoms. Here the authors succeed in measuring the EA of astatine, the heaviest naturally occurring halogen, and compare it with predictions from relativistic calculations.

The technological innovations in information processing and the increased storage capability have made possible to collect very large data sets in various fields of economics and finance. Researchers, companies, and governments look for ways to exploit this rich information. This special issue collects 11 papers who present state-of-the-art techniques to deal with many predictors, many regressors or many instruments. It grew out of the CIREQ conference on high dimensional models in econometrics organized by Marine Carrasco and Sílvia Gonçalves in Montreal, Canada, on May 4–5, 2012.

When estimating integrated volatilities based on high-frequency data, simplifying assumptions are usually imposed on the relationship between the observation times and the price process. In this paper, we establish a central limit theorem for the realized volatility in a general endogenous time setting. We also establish a central limit theorem for the tricity under the hypothesis that there is no endogeneity, based on which we propose a test and document that this endogeneity is present in financial data. Copyright © Cambridge University Press 2013.

When estimating integrated volatilities based on high-frequency data, simplifying assumptions are usually imposed on the relationship between the observation times and the price process. In this paper, we establish a central limit theorem for the realized volatility in a general endogenous time setting. We also establish a central limit theorem for the tricity under the hypothesis that there is no endogeneity, based on which we propose a test and document that this endogeneity is present in financial data. Copyright © Cambridge University Press 2013.

When estimating integrated volatilities based on high-frequency data, simplifying assumptions are usually imposed on the relationship between the observation times and the price process. In this paper, we establish a central limit theorem for the realized volatility in a general endogenous time setting. We also establish a central limit theorem for the tricity under the hypothesis that there is no endogeneity, based on which we propose a test and document that this endogeneity is present in financial data. Copyright © Cambridge University Press 2013.

The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. In particular, non-causal representations are not identified in the case of Gaussian AR processes. By contrast, in the infinite variance case, non-causal patterns can be identified and are relevant to describe different types of time series behavior. While maximum likelihood inference is known to be computationally demanding for the most popular families of heavy tailed distributions, some minimum distance approaches may be more tractable. The extended tests have negligible size distortion and more power than standard tests. The tests are applied to competing symmetric leptokurtic distributions with monthly return data on the US stock market index. These distributions are generally not picked as plausible alternatives, primarily because of the presence of skewness., SCOPUS: cp.j, info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published

The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. In particular, non-causal representations are not identified in the case of Gaussian AR processes. By contrast, in the infinite variance case, non-causal patterns can be identified and are relevant to describe different types of time series behavior. While maximum likelihood inference is known to be computationally demanding for the most popular families of heavy tailed distributions, some minimum distance approaches may be more tractable. The extended tests have negligible size distortion and more power than standard tests. The tests are applied to competing symmetric leptokurtic distributions with monthly return data on the US stock market index. These distributions are generally not picked as plausible alternatives, primarily because of the presence of skewness., SCOPUS: cp.j, info:eu-repo/semantics/published

This article deals with the estimation of the parameters of an α-stable distribution with indirect inference, using the skewed-t distribution as an auxiliary model. The latter distribution appears as a good candidate since it has the same number of parameters as the α-stable distribution, with each parameter playing a similar role. To improve the properties of the estimator in finite sample, we use constrained indirect inference. In a Monte Carlo study we show that this method delivers estimators with good properties in finite sample. We provide an empirical application to the distribution of jumps in the S&P 500 index returns. © 2010 Elsevier B.V. All rights reserved., SCOPUS: ar.j, info:eu-repo/semantics/published

Network of Information (NetInf) is a term coined for networks which unlike contemporary network are not node centric. As the name indicates, information supersedes nodes in the network. In this report, we propose an architecture of mobile node for NetInf. We call it NetInf Mobile Node. It is an extension of the basic node architecture proposed for NetInf. It is compatible to NetInf and TCP/IP based networks. The Virtual Node Layer modules in the architecture provide support for managing mobility, power consumption of the node as well data relaying/storing services. In- ner/Outer Locator Construction Routers (I/O LCTR) are two functions introduced in NetInf mobile nodes to operate between NetInf and non- NetInf sites. The basic purpose of NetInf mobile node is to maintain the QoS during mobility events. The handoff/handover are critical situations during mobility where chances of QoS degradation of an ongoing session are high. This report presents one such scenario in which QoS of an appli- cation is maintained during a handoff situations in heterogeneous wireless network environment through our proposed algorithm.

This article deals with the estimation of the parameters of an -stable distribution by the indirect inference method with the skewed-t distribution as an auxiliary model. The latter distribution appears as a good candidate for an auxiliary model since it has the same number of parameters as the -stable distribution, with each parameter playing a similar role. To improve the properties of the estimator in finite sample, we use a variant of the method called Constrained Indirect Inference. In a Monte Carlo study, we show that this method delivers estimators with good properties in finite sample. In particular they are much more efficient than two other prevalent methods based on the characteristic function and the empirical quantiles. We provide an empirical application to hedge fund returns.

info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published

info:eu-repo/semantics/published

International audience, By moving from its original host-centric architecture to a new information-centric organization, the Internet will be able to offer new services and applications to end users, allowing for example the on-demand composition of a new service from those already available online. However, this requires the development of a Network of Information to offer users the possibility to annotate, discover and access digital and real-world objects in a convenient way. As such, this paper presents an original architecture to support these new services based on three underlying spaces: a storage space to keep data resilient, an index space to allow efficient search and a communication space to ensure an efficient transfer of data between the different entities.

We develop in this paper a generalization of the Indirect Inference (II) to semi-parametric settings and termed Semi-parametric Indirect Inference (SII). We introduce a new notion of Partial Encompassing which lays the emphasis on Pseudo True Values of Interest. The main difference with the older notion of encompassing is that some components of the pseudo-true value of interest associated with the structural parameters do correspond to true unknown values. This enables us to produce a theory of robust estimation despite mis-specifications in the structural model being used as a simulator. We also provide the asymptotic probability distributions of our SII estimators as well as Wald Encompassing Tests (WET) and advocate the use of Hausman type tests on the required assumptions for the consistency of the SII estimators. We illustrate our theory with examples based on semi-parametric stochastic volatility models.