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A new multivariate heavy-tailed distribution is proposed as an extension of the univariate distribution of Politis (2004). The properties of the new distribution are discussed, as well as its effectiveness in modeling ARCH/GARCH residuals. A practical procedure for multi-parameter numerical maximum likelihood is also given, and a real data example is worked out.

In this paper, the effect of the FE discretization density on the results of both convex and nonconvex‐nonsmooth frictional contact and adhesive contact interface problems is investigated. The tool for this study is a variational formulation leading to an iterative method for the numerical solution of the arising nonconvex‐nonsmooth optimization problems. Various cases of monotone and nonmonotone interface laws are considered and interesting results are obtained.

We investigate predictive abilities of nonlinear models for stock returns when density forecasts are evaluated and compared instead of the conditional mean point forecasts. The aim of this paper is to show whether the in-sample evidence of strong nonlinearity in mean may be exploited for out-of-sample prediction and whether a nonlinear model may beat the martingale model in out-of-sample prediction. We use the Kullback–Leibler Information Criterion (KLIC) divergence measure to characterize the extent of misspecification of a forecast model. The reality check test of White (2000) using the KLIC as a loss function is conducted to compare the out-of-sample performance of competing conditional mean models. In this framework, the KLIC measures not only model specification error but also parameter estimation error, and thus we treat both types of errors as loss. The conditional mean models we use for the daily closing S&P 500 index returns include the martingale difference, ARMA, STAR, SETAR, artificial neural network, and polynomial models. Our empirical findings suggest the out-of-sample predictive abilities of nonlinear models for stock returns are asymmetric in the sense that the right tails of the return series are predictable via many of the nonlinear models, while we find no such evidence for the left tails or the entire distribution.

Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

Persons with intellectual disability and/or low-functioning autism spectrum didorder are with high support need (ID/ASD-HSN) are among the people who are most vulnerable to the COVID-19 pandemic. The specific vulnerability and the protective factors for persons with ID/LF-ASD attending residential and rehabilitative facilities have however received little attention. This paper aims to describe how two facilities located in the Italian COVID-19 red zone faced the risks associated with the spread of the pandemic and the results they have achieved so far.

Interventions to contrast the spread of the pandemic and preserve clients’ health conditions have been systematically monitored and recorded since the very beginning of the pandemic.

26/138 clients had to undergo clinical screening and laboratory tests for COVID-like symptomatology, but only one resulted affected by COVID-19 and survived. Considering that Lombardy had 89,595 cases and 16,262 deaths (January–May 2020), one COVID-19 case/138 clients is a good result. Temporarily limiting physical contacts with friends/family in favor of reducing the burden of risk and adopting a system of prevention/safety strategies directed for persons with ID/LF-ASD attending and their caregivers have been useful measures.

Structured or semi-structured interviews (using professional caregivers as informant) to confirm behavioral and emotional changes in the clients could not be carried out because of lack of time and resources (which were captured by the management of the pandemic) and could be the next goal for our residential facilities to implement the management of epidemic acute phases in a research-oriented view.

This study is a service evaluation report about facing COVID-19 pandemic. Only few such studies are present in medical literature about ID/ASD.

This chapter proposes a sociological reconstruction of the emergence of citizenship as a source of legitimacy for political institutions, and it focuses on examining the historical processes that first gave rise to this concept. It explains how citizenship has its origins in the transformation of feudal law, a process that culminated in patterns of military organization that characterized the rise of the early modern state in Europe. On this basis, it describes how the growth of constitutional democracy was integrally marked by the militarization of society and explains that military pressures have remained palpable in constitutional constructions of citizenship. In particular, it argues that, through the early growth of democracy, national citizenship practices were closely linked to global conflicts, and they tended to replicate such conflicts in national contexts. It concludes by showing how more recent processes of constitutional norm formation, based largely in international human rights law, have acted to soften the military dimensions of citizenship.

Recent scholarship in neo-evolutionary sociology has rejected stage-models in favor of multilinear theories that shift the study of sociocultural change away from teleological arguments toward those that emphasize selection pressures and macrodynamics. The paper below adopts a neo-evolutionary frame to revisit one of the most epochal moments in human sociocultural evolution, the urban revolution (about 5,000 years ago in Mesopotamia, China, Egypt, and perhaps the Indus Valley) and the rise of the first political units. Shifting the analysis from conventional perspectives, this paper asks the question why the polity was the first autonomous institution besides kinship and what consequences did this have on the trajectory of the human societies, and more generally, human sociocultural evolution. By doing so, a slightly different historiography is presented in which institutional autonomy corresponds not with stages, but rather an historical “phasing” that emphasizes the role that institutional entrepreneurs have played in driving institutional evolution via structural opportunities and historical contingencies.

We extend earlier work on the NoVaS transformation approach introduced by Politis (2003a, 2003b). The proposed approach is model-free and especially relevant when making forecasts in the context of model uncertainty and structural breaks. We introduce a new implied distribution in the context of NoVaS, a number of additional methods for implementing NoVaS, and we examine the relative forecasting performance of NoVaS for making volatility predictions using real and simulated time series. We pay particular attention to data-generating processes with varying coefficients and structural breaks. Our results clearly indicate that the NoVaS approach outperforms GARCH model forecasts in all cases we examined, except (as expected) when the data-generating process is itself a GARCH model.