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This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of weights accounts for the relevance of each conditional moment in defining the clusters. Robustness against outliers is also considered by extending the above clustering method using a suitable exponential transformation of the distance measure defined on the conditional higher moments. To show the usefulness of the proposed approach, we provide a study with simulated data and an empirical application to the time series of stocks included in the FTSEMIB 30 Index. [ABSTRACT FROM AUTHOR]

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor's contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability. [ABSTRACT FROM AUTHOR]

This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data. [ABSTRACT FROM AUTHOR]

Several studies deal with the development of advanced statistical methods for predicting football match results. These predictions are then used to construct profitable betting strategies. Even if the most popular bets are based on whether one expects that a team will win, lose, or draw in the next game, nowadays a variety of other outcomes are available for betting purposes. While some of these events are binary in nature (e.g. the red cards occurrence), others can be seen as binary outcomes. In this paper we propose a simple framework, based on score-driven models, able to obtain accurate forecasts for binary outcomes in soccer matches. To show the usefulness of the proposed statistical approach, two experiments to the English Premier League and to the Italian Serie A are provided for predicting red cards occurrence, Under/Over and Goal/No Goal events. [ABSTRACT FROM AUTHOR]

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity. [ABSTRACT FROM AUTHOR]

This paper deals with the challenging themes of the way sporting teams and athletes are ranked in sports competitions. Starting from the paradigmatic case of soccer, we advance a new method for ranking teams in the official national championships through computational statistics methods based on Kendall correlations and radar charts. In detail, we consider the goals for and against the teams in the individual matches as a further source of score assignment beyond the usual win-tie-lose trichotomy. Our approach overcomes some biases in the scoring rules that are currently employed. The methodological proposal is tested over the relevant case of the Italian “Serie A” championships played during 1930–2023. [ABSTRACT FROM AUTHOR]

The most common purpose of seasonal adjustment is to provide an estimate of the current trend so that judgmental short-term forecasts can be made. Bell (Proceedings of the American Statistical Association, 1995) formally considered how model-based seasonal adjustment could be done in order to facilitate the forecasting, showing that, from a theoretical perspective, this objective could be best served by not revising the data for seasonality. However, this study was lacking an empirical investigation to determine if this approach would realize any advantages when applied in practice. Aim of this paper is to assess whether is convenient, from forecasting perspective, to adjust data for seasonality or directly use a model which accounts for seasonality. In particular, we serve this scope by both a simulation study and an application with real data related to Industrial Production Index. We show that pre-adjusting the time series for seasonality allows for forecasting improvements in terms of accuracy. In the end we evaluated also the best seasonally adjustment method for forecasting purposes. Empirical evidence shows that the forecasts among seasonal adjustment methods are statistically different and that, while for the Italian TRAMO-SEATS outperform X13-ARIMA-SEATS, the opposite happen for the U.S. Industrial Production. Results from the simulation suggest that the forecasts among the considered seasonal adjustment methods are not statistically different for very short and long time period. However, seasonal adjustment methods lead to statistically different forecasts for medium and for very long time period. [ABSTRACT FROM AUTHOR]

Multicollinearity is one of the most important issues in regression analysis, as it produces unstable coefficients’ estimates and makes the standard errors severely inflated. The regression theory is based on specific assumptions concerning the set of error random variables. In particular, when errors are uncorrelated and have a constant variance, the ordinary least squares estimator produces the best estimates among all linear estimators. If, as often happens in reality, these assumptions are not met, other methods might give more efficient estimates and their use is therefore recommendable. In this paper, after reviewing and briefly describing the salient features of the methods, proposed in the literature, to determine and address the multicollinearity problem, we introduce the Lpmin method, based on Lp-norm estimation, an adaptive robust procedure that is used when the residual distribution has deviated from normality. The major advantage of this approach is that it produces more efficient estimates of the model parameters, for different degrees of multicollinearity, than those generated by the ordinary least squares method. A simulation study and a real-data application are also presented, in order to show the better results provided by the Lpmin method in the presence of multicollinearity. [ABSTRACT FROM AUTHOR]

This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007–2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years. [ABSTRACT FROM AUTHOR]

In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. In the case of financial time series, detecting clusters of volatility spillovers provides insights into the market structure, which can be useful to both portfolio managers and policy makers. We measure directional—i.e. “From” and “To” the others—volatility spillovers with a methodology based on the generalized forecast-error variance decomposition. Then, we propose a weighted fuzzy clustering model for grouping stocks with a similar degree of directional spillovers. By using a weighted approach, we allow the algorithm to decide which dimension of spillover is more relevant for clustering. Moreover, a robust clustering model is also proposed to alleviate the effect of possible outlier stocks. We apply the proposed clustering model for the analysis of spillover effects in the Italian stock market. [ABSTRACT FROM AUTHOR]

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation. [ABSTRACT FROM AUTHOR]