CIPS test for Unit Root in Panel Data: further Monte Carlo results

This paper analyzes, through Monte Carlo experiments, the behaviour of Pesaran's CIPS test for the null of a unit root in panel data when (i) the assumption of a single common factor in the specification of the cross-section dependence is violated and (ii) the autoregressive order of the residuals is estimated. The simulation analysis points to the single common factor as a fundamental assumption for a suitable behaviour of the CIPS test and suggests that the test delivers the best performance when the truncation lag is estimated as a deterministic function of the sample size.

Panel Unit Root Tests and the Specification of Cross-sectional Dependence

This paper analyzes, through Monte Carlo experiments, the robustness of several panel unit root tests to different specifications of the cross-sectional dependence. Since results show that the miss-specification of cross-correlation crucially affects the properties of the tests, a graphical approach is suggested in order to determine the model of dependence which is likely to have generated the original data.Panel unit root tests

On characterizations and tests of Benford’s law

Benford's law defines a probability distribution for patterns of significant digits in real numbers. When the law is expected to hold for genuine observations, deviation from it can be taken as evidence of possible data manipulation. We derive results on a transform of the significand function that provide motivation for new tests of conformance to Benford's law exploiting its sum-invariance characterization. We also study the connection between sum invariance of the first digit and the corresponding marginal probability distribution. We approximate the exact distribution of the new test statistics through a computationally efficient Monte Carlo algorithm. We investigate the power of our tests under different alternatives and we point out relevant situations in which they are clearly preferable to the available procedures. Finally, we show the application potential of our approach in the context of fraud detection in international trade

Multimodal MRI in Neurodegenerative Disorders

International audienc

The Robust Estimation of Monthly Prices of Goods Traded by the European Union

The general problem addressed in this document is the estimation of “fair” import prices from international trade data. The work is in support to the determination of the customs value at the moment of the customs formalities, to establish how much duty the importer must pay, and the post-clearance checks of individual transactions. The proposed approach can be naturally extended to the analysis of export flows and used for other purposes, including general market analyses. The Joint Research Centre of the European Commission has previously addressed (Arsenis et al., 2015) the trade price estimation problem by considering data for fixed product, origin and destination over a multiannual time period, typically of 3 or 4 years, leading to price estimates that are specific for each EU Member State. This report illustrates a different model whereby each price estimate is calculated on a monthly basis, using data for fixed time (month), product and origin. The approach differentiates between trades originated from different third countries and it is therefore particularly useful to monitor trends and anomalies in specific EU trade markets. These Estimated European Monthly Prices are publishes every month by the Joint Research Centre in a dedicated section of the THESEUS website (https://theseus.jrc.ec.europa.eu), accessible by authorized users of the EU and Member States services. The section, called Monthly Fair Prices, also shows the time evolution of worldwide price estimates computed with the same approach by fixing only time and product.JRC.I.3-Text and Data Minin

A new family of tempered distributions

Tempered distributions have received considerable attention, both from a theoretical point of view and in several important application fields. The most popular choice is perhaps the Tweedie model, which is obtained by tempering the Positive Stable distribution. Through tempering, we suggest a very flexible four-parameter family of distributions that generalizes the Tweedie model and that could be applied to data sets of non-negative observations with complex (and difficult to accommodate) features. We derive the main theoretical properties of our proposal, through which we show its wide application potential. We also embed our proposal within the theory of Lévy processes, thus providing a strengthened probabilistic motivation for its introduction. Furthermore, we derive a series expansion for the probability density function which allows us to develop algorithms for fitting the distribution to data. We finally provide applications to challenging real-world examples taken from international trade

Newcomb–Benford law and the detection of frauds in international trade

The contrast of fraud in international trade is a crucial task of modern economic regulations. We develop statistical tools for the detection of frauds in customs declarations that rely on the Newcomb–Benford law for significant digits. Our first contribution is to show the features, in the context of a European Union market, of the traders for which the law should hold in the absence of fraudulent data manipulation. Our results shed light on a relevant and debated question, since no general known theory can exactly predict validity of the law for genuine empirical data. We also provide approximations to the distribution of test statistics when the Newcomb–Benford law does not hold. These approximations open the door to the development of modified goodness-of-fit procedures with wide applicability and good inferential properties

Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State

When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals

A consensus guide to using functional near-infrared spectroscopy in posture and gait research

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is increasingly used in the field of posture and gait to investigate patterns of cortical brain activation while people move freely. fNIRS methods, analysis and reporting of data vary greatly across studies which in turn can limit the replication of research, interpretation of findings and comparison across works. RESEARCH QUESTION AND METHODS: Considering these issues, we propose a set of practical recommendations for the conduct and reporting of fNIRS studies in posture and gait, acknowledging specific challenges related to clinical groups with posture and gait disorders. RESULTS: Our paper is organized around three main sections: 1) hardware set up and study protocols, 2) artefact removal and data processing and, 3) outcome measures, validity and reliability; it is supplemented with a detailed checklist. SIGNIFICANCE: This paper was written by a core group of members of the International Society for Posture and Gait Research and posture and gait researchers, all experienced in fNIRS research, with the intent of assisting the research community to lead innovative and impactful fNIRS studies in the field of posture and gait, whilst ensuring standardization of research

Combining homogeneous groups of pre-classified observations with application to international trade

This article proposes three methods for merging homogeneous clusters of observations that are grouped according to a pre-existing (known) classification. This clusterwise regression problem is at the very least compelling in analyzing international trade data, where transaction prices can be grouped according to the corresponding origin–destination combination. A proper merging of these prices could simplify the analysis of the market without affecting the representativeness of the data and highlight commercial anomalies that may hide frauds. The three algorithms proposed are based on an iterative application of the F-test and have the advantage of being extremely flexible, as they do not require to predetermine the number of final clusters, and their output depends only on a tuning parameter. Monte Carlo results show very good performances of all the procedures, whereas the application to a couple of empirical data sets proves the practical utility of the methods proposed for reducing the dimension of the market and isolating suspicious commercial behaviors.JRC.E.1-Disaster Risk Managemen