Abstract
This study investigates the sustainability reporting practices of companies based in the Nordic and the Mediterranean European countries for the period 2013-2015. Its purpose is to analyse to what extent, if any, are there differences in these practices. It seeks to capture the influence of national institutions and firm specific characteristics in sustainability reporting. Non-parametric statistics are used to analyse some factors which influence disclosure, namely country, industry affiliation, type of property, listing status and size. In accordance with the theoretical frame used, that of the varieties of capitalism approach, findings suggest that in general companies from Mediterranean European countries present higher levels of engagement with the Global Reporting Initiative.

Abstract
Purpose: The purpose of this paper is to compare the way in which gender and propensity to risk are associated in two samples, one of entrepreneurs and the other of non-entrepreneurs, while controlling for other factors, namely, national cultures. Design/methodology/approach: On the basis of data from 19 advanced countries, and by using two different samples, one of entrepreneurs and the other of non-entrepreneurs, the authors have used logistical regression analysis to analyse the relation between gender and propensity to risk has been used. Findings: Findings suggest that gender and culture are much stronger in influencing risk propensity among non-entrepreneurs than among entrepreneurs. Originality/value: Instead of analysing the effects of propensity to risk in entrepreneurship, as is usually done, the authors study some of its determinants, highlighting the differences between men and women.

Abstract
The problem of joint estimation of parameters and state of continuous time systems using discrete time observations is addressed. The plant parameters are assumed to be modeled by a Wiener process. The a priori probability density function (pdf) of an extended state that comprises the plant state variables and the parameters is propagated in time using an approximate solution of the Fokker-Planck equation that relies on Trotter's formula for semigroup decomposition. The a posteriori (i. e., given the observations) pdf is then computed at the observation instants using Bayes law.

Abstract
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample data-sets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios.

Abstract
Floating car data (FCD) denotes the type of data (location, speed, and destination) produced and broadcasted periodically by running vehicles. Increasingly, intelligent transportation systems take advantage of such data for prediction purposes as input to road and transit control and to discover useful mobility patterns with applications to transport service design and planning, to name just a few applications. However, there are considerable quality issues that affect the usefulness and efficacy of FCD in these many applications. In this paper, we propose a methodology to compute such quality indicators automatically for large FCD sets. It leverages on a set of statistical indicators (named Yuki-san) covering multiple dimensions of FCD such as spatio-temporal coverage, accuracy, and reliability. As such, the Yuki-san indicators provide a quick and intuitive means to assess the potential "value" and "veracity" characteristics of the data. Experimental results with two mobility-related data mining and supervised learning tasks on the basis of two real-world FCD sources show that the Yuki-san indicators are indeed consistent with how well the applications perform using the data. With a wider variety of FCD (e.g., from navigation systems and CAN buses) becoming available, further research and validation into the dimensions covered and the efficacy of the Yuki-San indicators is needed.

Abstract
Autoregressive Moving Average (ARMA) time series model fitting is a procedure often based on aggregate data, where parameter estimation plays a key role. Therefore, we analyze the effect of temporal aggregation on the accuracy of parameter estimation of mixed ARMA and MA models. We derive the expressions required to compute the parameter values of the aggregate models as functions of the basic model parameters in order to compare their estimation accuracy. To this end, a simulation experiment shows that aggregation causes a severe accuracy loss that increases with the order of aggregation, leading to poor accuracy.