Fourier–type tests involving martingale difference processes

We develop testing procedures which detect if the observed time series is a martingale difference sequence. Furthermore, tests are developed that detect change–points in the conditional expectation of the series given its past. The test statistics are formulated following the approach of Fourier–type conditional expectations first proposed by Bierens (1982 Bierens, H. J. (1982). Consistent model speci?cation tests. J. Econometr. 20:105–134.[Crossref], [Web of Science ®] , [Google Scholar]) and have the advantage of computational simplicity. The limit behavior of the test statistics is investigated under the null hypothesis as well as under alternatives. Since the asymptotic null distribution contains unknown parameters, a bootstrap procedure is proposed in order to actually perform the test. The performance of the bootstrap version of the test is compared in finite samples with other methods for the same problem. A real–data application is also included.     

Mixture inverse Gaussian for unobserved heterogeneity in the autoregressive conditional duration model

In this paper, we assume that the duration of a process has two different intrinsic components or phases which are independent. The first is the time it takes for a trade to be initiated in the market (for example, the time during which agents obtain knowledge about the market in which they are operating and accumulate information, which is coherent with Brownian motion) and the second is the subsequent time required for the trade to develop into a complete duration. Of course, if the first time is zero then the trade is initiated immediately and no initial knowledge is required. If we assume a specific compound Bernoulli distribution for the first time and an inverse Gaussian distribution for the second, the resulting convolution model has a mixture of an inverse Gaussian distribution with its reciprocal, which allows us to specify and test the unobserved heterogeneity in the autoregressive conditional duration (ACD) model.

Our proposals make it possible not only to capture various density shapes of the durations but also easily to accommodate the behaviour of the tail of the distribution and the non monotonic hazard function. The proposed model is easy to fit and characterizes the behaviour of the conditional durations reasonably well in terms of statistical criteria based on point and density forecasts.     

Multivariate log-Birnbaum–Saunders regression models

In this paper, we present a multivariate version of the skewed log-Birnbaum–Saunders regression model. This new family of distributions holds good properties such as marginal variables following univariate skewed log-Birnbaum–Saunders distributions, besides presenting the usual log-Birnbaum–Saunders distribution as a particular case. Furthermore, the model parameters are estimated through maximum-likelihood methods, a closed-form expression for the Fisher’s information matrix is presented, and testing hypothesis for model parameters is performed. Two real datasets are analyzed and results are discussed.     

<Emphasis Type="BoldItalic">MAD-STEC</Emphasis>: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.     

Fast parallel $$\alpha $$-stable distribution function evaluation and parameter estimation using OpenCL in GPGPUs

\(\alpha \)-Stable distributions are a family of probability distributions found to be suitable to model many complex processes and phenomena in several research fields, such as medicine, physics, finance and networking, among others. However, the lack of closed expressions makes their evaluation analytically intractable, and alternative approaches are computationally expensive. Existing numerical programs are not fast enough for certain applications and do not make use of the parallel power of general purpose graphic processing units. In this paper, we develop novel parallel algorithms for the probability density function and cumulative distribution function—including a parallel Gauss–Kronrod quadrature—, quantile function, random number generator and maximum likelihood estimation of \(\alpha \)-stable distributions using OpenCL, achieving significant speedups and precision in all cases. Thanks to the use of OpenCL, we also evaluate the results of our library with different GPU architectures.     

Pension reforms in EU11 countries: An evaluation of post‐socialist pension policies

This article evaluates the pension policy pathways of the 11 former state socialist nations that have joined the European Union since 2004. Focusing primarily on the post‐2004 period, the analysis discusses the most important measurable outcomes of these countries’ pension reforms, in terms of poverty alleviation, pension adequacy and fiscal sustainability. Going beyond the quantifiable concepts, we also investigate the quality of the 11 countries’ pension systems in terms of equity as well as efficiency, emphasizing the less conspicuous design errors present in these systems. Although these errors have received little attention to date, they may harm pension schemes along several dimensions, including their fiscal sustainability.     

Housing of Seniors in the Czech Republic: Challenges for Social Services

The article focuses on the housing market, the behavior and motivations of senior households to move or to stay in place. Knowing if and why seniors decide to move at retirement is a critical factor for the establishment of social service policies in terms of their structure, location, and provision.

This study uses secondary data based on information about Czech households collected by the Czech Statistical Office (CSO). The data are annually collected via sample surveys of the income and living conditions of households (EU-SILC). The sample covers more than eight thousands of households. Analyzed data cover the period 2007–2012 when the abolishment of rent regulation in the Czech Republic took place. It is hypothesized that an impact like this might increase the willingness to move and reveal the factors which underlie the decisions of particular households.

The results indicated that most Czech households that decided to move during the study period were driven by the increased financial burden of housing. Other factors, including the availability of social services and public utilities within the current location, played only minor roles. It seems that Czech senior households act in a very pragmatic and rational manner when deciding whether to stay in place or move, with the majority of households preferring not to move. Social policies should, therefore, concentrate on providing services for the current locations rather than on the construction of new social housing.     

Promoting healthy transition to college through mindfulness training with first-year college students: Pilot randomized controlled trial

Objective: Given the importance of developmental transitions on young adults' lives and the high rates of mental health issues among U.S. college students, first-year college students can be particularly vulnerable to stress and adversity. This pilot study evaluated the effectiveness and feasibility of mindfulness training aiming to promote first-year college students' health and wellbeing. Participants: 109 freshmen were recruited from residential halls (50% Caucasian, 66% female). Data collection was completed in November 2014. Methods: A randomized control trial was conducted utilizing the Learning to BREATHE (L2B) program, a universal mindfulness program adapted to match the developmental tasks of college transition. Results: Participation in the pilot intervention was associated with significant increase in students' life satisfaction, and significant decrease in depression and anxiety. Marginally significant decrease was found for sleep issues and alcohol consequences. Conclusions: Mindfulness-based programs may be an effective strategy to enhance a healthy transition into college.     

Efficiency Analysis of the European Food Banks: Some Managerial Results

Hundreds of thousands of people in Europe are daily receiving food, thanks to local food banks, run basically by volunteers who collect donations and distribute them to organizations. In this paper, data from a sample of food banks working in 13 European countries are analysed using Data Envelopment Analysis (DEA), trying to learn about their profiles and find some clues about the efficiency of their operations, comparing them according to variables such as the number of volunteers and permanent staff, the tonnage of food delivered and the number of people served. Significant inefficiencies were found in the sector as a result of high performance of some food banks, thus setting a high level standard of operational efficiency. Some additional results regarding food bank sizes and performance are presented.