The reversal of the pension reform 1998 from a short-term perspective

The private pension pillar established in 1998 has generated equally important short-, medium- and long-term effects. This article addresses the short- and medium-term time horizons, which differ from one another. In the short term, the government lost contribution revenues, while its pension expenditures did not decrease. However, since this shortfall in state revenues did not influence the consumption and savings decisions of households, in this analysis the short-term indicators of fiscal impact on demand disregard the impact of lost revenues. In the medium term, however, the rechanneling of contributions increased public debt and household savings. Consequently, similarly to official statistics, our household indicators and our medium-term fiscal indicator (augmented SNA deficit) take into account the effect of lost revenues. As the vast majority of members returned to the state pension pillar in 2011, for the purposes of our analysis, we could well assume that the private pension pillar never existed. Accordingly, the difference between our medium-term fiscal indicator and the short-term indicator disappears. As a result, we have changed our household indicators retroactively in such a way as if the contributions and the returns they yielded had always belonged to the state. This was necessary because the official statistics do not spread this amount over time, but account for it in full for 2011 as a capital transfer between households and the general government, which renders evaluation of the developments extremely difficult.pension reform, savings, pension funds, statistical correction, net lending, deficit.

Long-Term Physical and Mental Health Effects of Domestic Violence

Domestic violence is an issue affecting people of all ages, races, genders, and sexual orientations. Violence against men and same-sex domestic violence are often considered less of a threat to society and to the people involved, but it is important to understand that male-on-female violence, female-on-male violence, and same-sex violence all involve serious consequences to the victim’s and batterer’s short- and long-term health. This paper determines whether men or women suffer from more long-term health problems caused by domestic violence by comparing the currently published statistics on the prevalence of domestic violence in heterosexual and homosexual relationships, and analyzing the results of existing studies on the short- and long-term health effects of domestic violence. The findings indicate that although men and women sustain many of the same injuries, women suffer from more long-term health problems caused by domestic violence

Dynamical estimates of chaotic systems from Poincar\'e recurrences

We show that the probability distribution function that best fits the distribution of return times between two consecutive visits of a chaotic trajectory to finite size regions in phase space deviates from the exponential statistics by a small power-law term, a term that represents the deterministic manifestation of the dynamics, which can be easily experimentally detected and theoretically estimated. We also provide simpler and faster ways to calculate the positive Lyapunov exponents and the short-term correlation function by either realizing observations of higher probable returns or by calculating the eigenvalues of only one very especial unstable periodic orbit of low-period. Finally, we discuss how our approaches can be used to treat data coming from complex systems.Comment: subm. for publication. Accepted fpr publication in Chao

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product

METHODOLOGICAL PROPOSAL FOR COMPILING THE ILO UNEMPLOYMENT WITH MONTHLY PERIODICITY

Development of methodology for deriving the monthly unemployment statistics directly from the quarterly Labour Force Survey (LFS) results by econometric modeling meets the requirements of insuring the information on short-term needed for employment policies, aiming to achieve the objectives of Europe 2020. Estimated monthly data series according to the methodology allow assessment of short-term trends in unemployment measured according to the criteria of the International Labour Organisation (ILO) in terms of comparability with European statistics