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With the increase of social media usage, a huge new source of data has become available. Despite the enthusiasm linked to this revolution, one of the main outstanding criticisms in using these data is selection bias. Indeed, the reference population is unknown. Nevertheless, many studies show evidence that these data constitute a valuable source because they are more timely and possess higher space granularity. We propose to adjust statistics based on Twitter data by anchoring them to reliable official statistics through a weighted, space-time, small area estimation model. As a by-product, the proposed method also stabilizes the social media indicators, which is a welcome property required for official statistics. The method can be adapted anytime official statistics exists at the proper level of granularity and for which social media usage within the population is known. As an example, we adjust a subjective well-being indicator of "working conditions" in Italy, and combine it with relevant official statistics. The weights depend on broadband coverage and the Twitter rate at province level, while the analysis is performed at regional level. The resulting statistics are then compared with survey statistics on the "quality of job" at macro-economic regional level, showing evidence of similar paths. [ABSTRACT FROM AUTHOR]

At the end of 1990s, Danny Quah devoted several papers to the analysis of polarization and stratification in the convergence processes of economies, creating the image of the 'convergence clubs' and suggesting the importance of studying the distribution dynamics of the macroeconomic variables. As for the labour markets, Overman and Puga (2002) showed that a progressive polarization of unemployment was in fact occurring among the European regions in 1986-1996, causing a phenomenon of cross-border clusterization. Here we propose to analyse the evolution of the unemployment rates of the EU 27 regions in the last two decades assuming that the unemployment rates evolve according to a Gompertz stochastic process. The estimated parameters of the process - intrinsic growth rate, deceleration factor, volatility - represent the evolutionary path of the unemployment rate and allow for estimating the steady state of the process. A cluster analysis is performed on the steady state values of the unemployment rates. The analysis confirms the emergence of several 'convergence clubs' among the European regional labour markets, which are compared to the clusters resulting from the more traditional clusterization on the current unemployment rates. [ABSTRACT FROM AUTHOR]

Abstract: We consider a multidimensional Itô process with some unknown drift coefficient process and volatility coefficient with covariate process , the function being known up to . For this model, we consider a change point problem for the parameter in the volatility component. The change is supposed to occur at some point . Given discrete time observations from the process , we propose quasi-maximum likelihood estimation of the change point. We present the rate of convergence of the change point estimator and the limit theorems of the asymptotically mixed type. [Copyright &y& Elsevier]

Abstract: In this paper we propose the use of as test statistics to verify simple hypotheses about a one-dimensional parametric diffusion process , from discrete observations with , , under the asymptotic scheme , and . The class of is wide and includes several special members like Kullback–Leibler, Rényi, power and . We derive the asymptotic distribution of the test statistics based on the estimated . The asymptotic distribution depends on the regularity of the function and in general it differs from the standard distribution as in the i.i.d. case. Numerical analysis is used to show the small sample properties of the test statistics in terms of estimated level and power of the test. [Copyright &y& Elsevier]

Abstract: In this paper, we do a comparative simulation study of the standard empirical distribution function estimator versus a new class of nonparametric estimators of a distribution function F, called the iterated function system (IFS) estimator. The target distribution function F is supposed to have compact support. The IFS estimator of a distribution function F is considered as the fixed point of a contractive operator T defined in terms of a vector of parameters p and a family of affine maps which can be both dependent on the sample . Given , the problem consists in finding a vector p such that the fixed point of T is “sufficiently near” to F. It turns out that this is a quadratic constrained optimization problem that we propose to solve by penalization techniques. Analytical results prove that IFS estimators for F are asymptotically equivalent to the empirical distribution function (EDF) estimator. We will study the relative efficiency of the IFS estimators with respect to the empirical distribution function for small samples via the Monte Carlo approach. For well-behaved distribution functions F and for a particular family of the so-called wavelet maps the IFS estimators can be dramatically better than the empirical distribution function in the presence of missing data, i.e. when it is only possible to observe data on subsets of the whole support of F. [Copyright &y& Elsevier]

This article illustrates an approach to forecasting change in conflict fatalities designed to address the complexity of the drivers and processes of armed conflicts. The design of this approach is based on two main choices. First, to account for the specificity of conflict drivers and processes over time and space, we model conflicts in each individual country separately. Second, we draw on an adaptive model—Dynamic Elastic Net, DynENet—which is able to efficiently select relevant predictors among a large set of covariates. We include over 700 variables in our models, adding event data on top of the data features provided by the convenors of the forecasting competition. We show that our approach is suitable and computationally efficient enough to address the complexity of conflict dynamics. Moreover, the adaptive nature of our model brings a significant added value. Because for each country our model only selects the variables that are relevant to predict conflict intensity, the retained predictors can be analyzed to describe the dynamic configuration of conflict drivers both across countries and within countries over time. Countries can then be clustered to observe the emergence of broader patterns related to correlates of conflict. In this sense, our approach produces interpretable forecasts, addressing one key limitation of contemporary approaches to forecasting. [ABSTRACT FROM AUTHOR]

In this article, we construct a sequence of discrete‐time stochastic processes that converges in the Skorokhod metric to a COGARCH(p,q) model. The result is useful for the estimation of the COGARCH(p,q) on irregularly spaced time series data. The proposed estimation procedure is based on the maximization of a pseudo log‐likelihood function and is implemented in the yuima package. [ABSTRACT FROM AUTHOR]

The rise of social network sites reopened the debate on the ability of traditional media to influence public opinion and act as an agenda setter. To answer this question, the present paper investigates first-level and second-level agenda-setting effects in the online environment by focusing on two heated Italian political debates (the reform of public funding of parties and the debate over austerity). By employing innovative and efficient statistical methods such as the lead–lag analysis and supervised sentiment analysis, we compare the attention devoted to each issue and the content spread by online news media and Twitter users. Our results show that online media keep their first-level agenda-setting power even though we find a marked difference between the slant of online news and the Twitter sentiment. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing attention in the literature on the possibility of analyzing social media as a useful complement to traditional off-line polls to monitor an electoral campaign. Some scholars claim that by doing so, we can also produce a forecast of the result. Relying on a proper methodology for sentiment analysis remains a crucial issue in this respect. In this work, we apply the supervised method proposed by Hopkins and King to analyze the voting intention of Twitter users in the United States (for the 2012 Presidential election) and Italy (for the two rounds of the centre-left 2012 primaries). This methodology presents two crucial advantages compared to traditionally employed alternatives: a better interpretation of the texts and more reliable aggregate results. Our analysis shows a remarkable ability of Twitter to “nowcast” as well as to forecast electoral results. [ABSTRACT FROM AUTHOR]

The growing usage of social media by a wider audience of citizens sharply increases the possibility of investigating the web as a device to explore and track political preferences. In the present paper we apply a method recently proposed by other social scientists to three different scenarios, by analyzing on one side the online popularity of Italian political leaders throughout 2011, and on the other the voting intention of French Internet users in both the 2012 presidential ballot and the subsequent legislative election. While Internet users are not necessarily representative of the whole population of a country’s citizens, our analysis shows a remarkable ability for social media to forecast electoral results, as well as a noteworthy correlation between social media and the results of traditional mass surveys. We also illustrate that the predictive ability of social media analysis strengthens as the number of citizens expressing their opinion online increases, provided that the citizens act consistently on these opinions. [ABSTRACT FROM PUBLISHER]