Population aging, social security and fiscal limits

We use an overlapping generations (OLG) life-cycle model with distortionary taxation on labor and capital to derive a threshold dependency ratio, i.e. a point in the cross-section distribution of the population beyond which tax revenues can no longer sustain the planned level of transfers to retirees. We quantify the level of the threshold; the distance of the economy from the threshold; and the probability of reaching the threshold at some point in the future. The model is calibrated on the United States and fourteen European countries which have dependency ratios among the highest in the world. We examine the effects on the threshold and welfare of a number of policies often advocated to improve the sustainability of pension systems. New tax data on dynamic Laffer effects are provided

Transient behavior of fractional queues and related processes

We propose a generalization of the classical M/M/1 queue process. The resulting model is derived by applying fractional derivative operators to a system of difference-differential equations. This generalization includes both non-Markovian and Markovian properties, which naturally provide greater flexibility in modeling real queue systems than its classical counterpart. Algorithms to simulate M/M/1 queue process and the related linear birth-death process are provided. Closed-form expressions of the point and interval estimators of the parameters of these fractional stochastic models are also presented. These methods are necessary to make these models usable in practice. The proposed fractional M/M/1 queue model and the statistical methods are illustrated using S&P data

A componential approach to individual differences in hypnotizability

Although responsiveness to hypnotic suggestions (hypnotizability) typically is conceptualized and studied as a singular homogeneous capability, numerous lines of evidence suggest instead that it is a hierarchically structured cognitive capacity comprising a core superordinate ability and ancillary subordinate component abilities. After reviewing current approaches to the measurement of hypnotizability and componential approaches to other cognitive capabilities, we highlight outstanding questions in the field and argue for a componential approach to the study of hypnotizability. Such an approach assumes that hypnotizability is not a unitary construct but is rooted in multiple subabilities that interact to give rise to individual differences that are expressed within specific contexts. We revisit previous componential work on hypnotizability and propose a series of steps by which a componential model can be more rigorously interrogated and integrated with contemporary advances in our understanding of human cognition

Modulation of microRNA editing, expression and processing by ADAR2 deaminase in glioblastoma.

Background: ADAR enzymes convert adenosines to inosines within double-stranded RNAs, including microRNA (miRNA) precursors, with important consequences on miRNA retargeting and expression. ADAR2 activity is impaired in glioblastoma and its rescue has anti-tumoral effects. However, how ADAR2 activity may impact the miRNome and the progression of glioblastoma is not known. Results: By integrating deep-sequencing and array approaches with bioinformatics analyses and molecular studies, we show that ADAR2 is essential to edit a small number of mature miRNAs and to significantly modulate the expression of about 90 miRNAs in glioblastoma cells. Specifically, the rescue of ADAR2 activity in cancer cells recovers the edited miRNA population lost in glioblastoma cell lines and tissues, and rebalances expression of onco-miRNAs and tumor suppressor miRNAs to the levels observed in normal human brain. We report that the major effect of ADAR2 is to reduce the expression of a large number of miRNAs, most of which act as onco-miRNAs. ADAR2 can edit miR-222/221 and miR-21 precursors and decrease the expression of the corresponding mature onco-miRNAs in vivo and in vitro, with important effects on cell proliferation and migration. Conclusions: Our findings disclose an additional layer of complexity in miRNome regulation and provide information to better understand the impact of ADAR2 editing enzyme in glioblastoma. We propose that ADAR2 is a key factor for maintaining edited-miRNA population and balancing the expression of several essential miRNAs involved in cancer

Modelling the U.S. sovereign credit rating

This paper proposes a new methodology for generating sovereign credit ratings. These are determined by mapping the probability that the debt-GDP ratio might exceed a maximum debt limit at some point in the future into a credit rating. The debt limit can be either ad hoc or based on the financial ability of a government to change fiscal policy in the future to meet its outstanding obligations. When applied to quarterly U.S. data from 1970 to 2011, two clear instances are found in which the U.S. sovereign credit rating would have been downgraded on this basis: during the 1970s oil crisis and in the aftermath of the Lehman collapse in 2008. This result is robust to several alternative views on the maximum borrowing capacity of the U.S. economy

Tackling large outliers in macroeconomic data with vector artificial neural network autoregression

We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure non-explosive dynamics. As such, it is employable to account for nonlinearity in macroeconomic dynamics not only during typical business cycles but also in a wide range of extreme events, like deep recessions and strong expansions. The methodology is put to the test using aggregate data for the United States that include the abnormal realizations during the recent Covid-19 pandemic. The model delivers plausible and stable structural inference, and accurate out-of-sample forecasts. This performance compares favourably against a number of alternative methodologies recently proposed to deal with large outliers in macroeconomic data caused by the pandemic

Multi-instrument observations of a failed flare eruption associated with MHD waves in a loop bundle

Context. We present observations of a B7.9 class flare that occurred on January 24th, 2015, using SDO/AIA, Hinode/EIS and XRT. The flare triggers an eruption of a dense cool plasma blob as seen in AIA 171Å which is unable to completely break out and remains confined within a local bundle of active region loops. During this process, transverse oscillations of the threads are observed. The cool plasma is then observed to descend back to the chromosphere along each loop strand. At the same time, a larger diffuse co-spatial loop observed in the hot wavebands of SDO/AIA and Hinode/XRT is formed, exhibiting periodic intensity variations along its lenght. Aims. The formation and evolution of magnetohydrodynamic (MHD) waves depend upon the values of the local plasma parameters (e.g., density, temperature, magnetic field) which can hence be inferred by coronal seismology. In this study we aim to assess how the observed MHD modes are affected by the variation of density and temperature. Methods. We combine analysis of EUV/X-ray imaging and spectroscopy using SDO/AIA, Hinode/EIS and XRT. Results. The transverse oscillations of the cool loop threads are interpreted in terms of vertically polarised kink oscillations. The fitting procedure provides estimates for the period of about 3.5–4 min, and the amplitude of ∼ 5 Mm. The oscillations are strongly damped showing very low quality factor (1.5–2), which is defined as the ratio of the damping time and the oscillation period. The weak variation of the period of the kink wave, which is estimated from the fitting analysis, is in agreement with the density variations due to the presence of the plasma blob inferred from the intensity light curve at 171Å. The coexisting intensity oscillations along the hot loop are interpreted as a slow MHD wave with the period of 10 min and phase speed of about 436 km s−1 . Comparison between the fast and slow modes allows for the determination of the Alfvén speed, and consequently magnetic field values. The plasma-β inferred from the analysis is estimated to be around 0.1–0.3. Conclusions. We show that the evolution of the detected waves is determined by the temporal variations of the local plasma parameters, caused by the flare heating and the consequent cooling. We apply coronal seismology to both waves obtaining estimations of the background plasma parameter