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Hospitals are complex organizations which provide a wide array of health care services. This complexity creates challenges for stakeholders who wish to use financial accounting statements to make inferences about the productive choices made by a hospital's management. These challenges are especially salient when using data reported at the department (or cost center) level, or where the provision of care is coordinated across hospital departments. This study applies information entropy-based comparability analysis techniques to overall and department-level hospital financial data to identify hospital peer groups. Hospitals peer groups not only exhibit similar financial positions overall, but are also likely to exhibit operational similarities at the department level. Data for this analysis are drawn from the financial statements of Washington State critical access hospitals in the fiscal year 2019. The medical laboratory and pharmacy departments were specifically assessed because their services impact or support virtually every other revenue-producing department in the hospital. Findings suggest both departments significantly impact the formation of peer groups, with the pharmacy department contributing the largest impact.

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Cavitation erosion is one of the most severe problems encountered in hydraulic turbomachinery. When testing the materials, the engineers usually rely on standardized procedures. The most common one being the vibratory ASTM G-32 test, which offers two possibilities of performing the test - the direct, where the specimen is attached to the ultrasonic device and the indirect, where the specimen is stationary and exposed to the ultrasonic horn, positioned just 0.5 mm from it. The erosion rates from the two are significantly different and a question may be asked if they are at all comparable and further on are they comparable to the "real-life" hydrodynamic cavitation which occurs in turbomachinery. In this study we performed erosion tests on a stationary specimen where the gap between the specimen and the horn was varied from 0.3 to 4 mm. In addition, we used high speed visualization to observe the cavitation in the gap. We observed that the cavitation erosion rate strongly depends on the gap. From visualization we see that the cavitation dynamics significantly changes in a small gap, leading to a large, but 2-dimensional cavitation bubbles which collapse very slowly, compared to the small spherical ones in a larger gap. We investigated the probability of shock wave occurrence and derived a very simple model, which gives accurate qualitative predictions of experimental data. Finally, the study puts into question the validity of ASTM G32 test - the most common approach used in engineering today.

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We propose a new methodology to asses risk spillovers in a time-series framework. Firstly, we introduce an explicit nonparametric measure of cross-sectional conditional tail co-movement, which is intuitively comparable to the Conditional Value-at-Risk (CoVaR). We show that nonlinear CoVaR (NCoVaR) is able to capture even highly nonlinear dependence structures. Secondly, for the purpose of potential contagion analysis, we adapt the measure to be informative about the causality direction between the variables in the Granger causality sense. By showing that the natural estimators of the two metrics are U-statistics, we construct formal nonparametric tests for independence and Granger non-causality. Numerical simulations confirm that in common situations the nonparametric tests have better size and power properties than their parametric counterparts. The methodology is illustrated empirically by assessing risk transmissions between sovereigns and banking sectors in the euro area, which observed highly irregular co-movements between asset prices after the global financial crisis. The new measures seem to be less susceptible to these irregularities than their parametric analogues, providing a clearer overview of the underlying sovereign-bank risk feedback loops.

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In the context of the slowing growth of the real economy and the rapid development of the financial industry, more and more non-financial companies are participating in the financial industry for the purpose of development and profit expansion. China has gradually appeared the phenomenon of corporate financialization. This paper uses the panel fixed effect model empirically examines the effect of political turnover on corporate financialization by using data of listed companies and top prefecture level officials in China between 2007 and 2020. We find that the turnover of mayors significantly decreases corporate financialization, while the turnover of party secretaries has no impact on corporate financialization. Moreover, these results are moderated by the characteristics of government officials and firm's characteristics. Our results further show that changes by mayors increase fixed asset investment and decrease cash holdings, and, thus, reduce corporate financialization. These findings could assist in solving the "from real to virtual" problems, strengthening financial services, and realizing high-quality economic development.

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We utilize several unique firm-level datasets in order to assess the efficiency and effectiveness of the government support aiming to curb the economic consequences of the coronavirus pandemic. The results, drawing on the experience of a small open European country, suggest the distributed COVID-19 subsidies save non-negligible number of jobs and sustain economic activity during the first wave of the pandemic. General distribution rules designed on the fly may bring close to optimal results in terms of the support allocation, as relatively more productive, privately owned, foreign-demand oriented firms are prioritized and firms with a high environmental footprint or zombie firms record a relatively lower chance of obtaining government funding. By assuming constant cost elasticities to sales, we show that the pandemic deteriorates strongly firm profits and increases significantly the share of illiquid and insolvent firms. Government wage subsidies somewhat mitigate firm losses and have statistically significant effect, but relatively mild compared to the size of the economic shock. Our estimates also confirm that larger firms, receiving smaller relative size of the support, have more space to cover their additional liquidity needs by increasing trade liabilities or liabilities to affiliated entities, while SMEs face higher risk of insolvencies.

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In recent studies, numerous anomalies against the weak and semi-strong-forms of efficient market hypothesis (EMH) have been found insignificant after controlling the small-firm effect. We investigate whether the insider trading anomaly, a major anomaly against the strong-form of EMH, can survive after excluding small firms with a novel data set (FTSE-350) and document several new findings. We find a substantially larger number of insider purchases than sales, while the average volume of insider sales is much higher than the average volume of insider purchases. Echoing recent US studies, we find that insider sales generate more abnormal returns than insider purchases do. We find much lower abnormal returns from insider trading than documented in the literature and the associated trading costs, which suggests that the market efficiency of individual stocks may depend on their sizes, and even the strong-form of EMH holds to a larger extent than previously recognized.

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In this article, we propose a novel estimator of extreme conditional quantiles in partial functional linear regression models with heavy-tailed distributions. The conventional quantile regression estimators are often unstable at the extreme tails due to data sparsity, especially for heavy-tailed distributions. We first estimate the slope function and the partially linear coefficient using a functional quantile regression based on functional principal component analysis, which is a robust alternative to the ordinary least squares regression. The extreme conditional quantiles are then estimated by using a new extrapolation technique from extreme value theory. We establish the asymptotic normality of the proposed estimator and illustrate its finite sample performance by simulation studies and an empirical analysis of diffusion tensor imaging data from a cognitive disorder study.

Dans cet article, un nouvel estimateur de quantiles conditionnels extrêmes est élaboré dans le cadre de modèles de régression linéaire fonctionnelle partielle avec des distributions à queues lourdes. Il est bien connu que la rareté des observations dans les ailes extrêmes de distributions à queues lourdes rend souvent les estimateurs de régression quantile usuels instables. Pour parer à la non robustesse des moindres carrés classiques, les auteurs ont commencé par estimer la fonction de pente et le coefficient partiellement linéaire d'une régression quantile en ayant recours à une approche basée sur l'analyse en composantes principales fonctionnelles. Ensuite, ils ont estimé les quantiles conditionnels extrêmes à l'aide d'une nouvelle technique d'extrapolation issue de la théorie des valeurs extrêmes. En plus d'établir la normalité asymptotique de l'estimateur proposé, les auteurs illustrent ses bonnes performances à distance finie par le biais d'une étude de simulation et une mise en oeuvre pratique sur les données d'imagerie de diffusion par tenseurs provenant d'une étude portant sur des troubles cognitifs.

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Extreme Value Theory (EVT) aims to study the tails of probability distributions in order to measure and quantify extreme events of maximum and minimum. In river flow data, an extreme level of a river may be related to the level of a neighboring river that flows into it. In this type of data, it is very common for flooding of a location to have been caused by a very large flow from an affluent river that is tens or hundreds of kilometers from this location. In this sense, an interesting approach is to consider a conditional model for the estimation of a multivariate model. Inspired by this idea, we propose a Bayesian model to describe the dependence of exceedance between rivers, where we considered a conditionally independent structure. In this model, the dependence between rivers is captured by modeling the excess marginally of one river as a consequence of linear functions of the other rivers. The results showed that there is a strong and positive connection between excesses in one river caused by the excesses of the other rivers.

RESUMEN

The main objective of Statistics of Extremes is the estimation of probabilities of rare events. When extending the analysis of the limiting behaviour of the extreme values from independent and identically distributed sequences to stationary sequences a key parameter appears, the extremal index θ, whose accurate estimation is not easy. Here we focus on the estimation of θ using blocks estimators, that can be constructed by using disjoint or sliding blocks. The asymptotic properties for both procedures were studied and compared but both blocks estimators require the choice of a threshold and a block length. Some criteria have appeared for the choice of those nuisance quantities but some research is still needed. We will show how the threshold and the block size choices can affect the estimates. However the main objective of this work is to revisit another estimation procedure that only depends on the block length, although some conditions on the underlying process need to be verified. The associated estimator presents nice asymptotic properties, and for finite samples is here illustrated a stability criterion for choosing the block length and then obtaining the θ estimate. A large simulation study has been performed and an application to daily mean flow discharge rate in the hydrometric station of Fragas da Torre in river Paiva, data collected from 1 October 1946 to 30 April 2012 is done.

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The data presented in this article are related to the research article entitled "Do shareholder coalitions affect agency costs? Evidence from Italian-listed companies", Research in International Business and Finance, Forthcoming (Rossi et al., 2018) [1]. The study shows an empirical analysis using an extensive balanced panel dataset of 163 Italian listed companies for the period 2002-2013, which is a sample yielding 1956 firm-year observations. The sample consists primarily of manufacturing firms, but also includes some service enterprises. However all financial firms and regulated utilities are excluded. We collected data on ownership structure for the entire study period. Information was acquired from the Consob website and the individual company reports on corporate governance. Data on firm-level indicators (debt-to-capital ratio, firm size, and age of the firm) for all companies in the sample were collected from Datastream, Bloomberg, and Calepino dell'Azionista, as well as obtained manually from the financial statements of the individual companies being studied. Our dataset contains several measures of ownership structure for Italian listed companies.