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Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding both alternative predictors (e.g., interest rates, price levels) and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach with an application to CAD/USD exchange rate predictions.

In this paper, we propose a new approach for clustering time series showing similar time-varying moments. At this aim, we compute a dissimilarity measure assuming that the estimated conditional moments are continuous functions indexed by time. Conditional moments based clustering allows to obtain different classifications according to the data distribution’s parameters. We show the usefulness of the proposed clustering procedure with an application to the financial time series in the DAX30 index

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.

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.

In this paper we propose a clustering approach based on the DISTATIS algorithm for the study of time series showing spatial spillover effects. This clustering approach involves the use of a spatio-temporal distance, obtained from the combination of temporal and spatial distances. An application to real time series, related to Euro Area business cycles, enriches this paper.

Agent-based models are computational approaches used to reproduce the interactions between economic agents. These models are widely applied in many contexts to get deeper understanding about agents’ behaviors within complex systems. In this paper, we provide a bibliometric analysis about agent-based models in finance and, considering bibliographic coupling, we identify the presence of two distinct clusters of research communities, i.e., financial economics and econophysics. Cluster-specific thematic analyses are conducted to understand if the two communities are characterized by different emerging and motor topics. By highlighting several differences in the clusters, we also show the two research communities specialized in different specific topics.

Well-being is a multidimensional concept that cannot be described using a single indicator. By the synthesis of different dimensions it is possible to obtain composite indicators (CIs). Principal Components Analysis (PCA) is one of the most popular multivariate statistical techniques used building CIs. However, the fact that PCA does not take into account the spatial dimension of the phenomenon makes it unsuitable for studying the well-being of urban areas. In this paper, we propose to use Spatial Principal Component Analysis (SPCA) for measuring well-being. The SPCA technique provides principal component scores that summarize both the spatial variability and the spatial autocorrelation structure among the statistical units. In this paper, the SPCA is used to construct well-being CIs for the Italian provinces.

The SARS-Cov-2 has spread differently over space and time worldwide. By monitoring the contagion’s time evolution, the November 3 2020 the Italian government introduced differentiated regime of restrictions among its regions. This experiment demonstrated that public health policies can be effectively designed by means of clustering. This paper proposes a fuzzy clustering model where spatial and temporal dimensions of the disease spread are optimally weighted. The resulting model is applied with the aim of identifying groups of Italian regions with similar contagion spread. We found that two groups of regions sharing similar patterns of COVID-19 spread over both space and time exist. Appropriate public health policies can be designed on the basis of this evidence.

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

Composite confidence indicators are widely used to nowcast GDP. In this paper, we aim to construct a new composite confidence indicator which weighting scheme reflects the impact of consumer and business confidence on economic conditions. While GDP is quarterly measured, confidence indicators are monthly recorded. Our approach allows us to deal with data sampled at different frequencies. In particular, we propose a weighting scheme estimation based on U-MIDAS regression techniques.

Business cycle volatility has been extensively studied by means of the well-known ARCH and GARCH processes. Aim of this paper is to show that the score-driven models are instead more accurate in predicting business cycle volatility than the GARCH-type models. Motivated by fact that the empirical evidence do not support the hypothesis of Gaussianity also for business cycles, we assume the Generalized Error Distribution and its extension for skewness in estimating the volatility models within the GARCH framework. After reviewing the basic properties of the score-driven approaches, we carry out an empirical analysis with respect to the business cycles of the United States and Japan. We show that the score-driven models provide superior performances than both Gaussian and non-Gaussian GARCH processes in forecasting business cycle volatility.

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.

Recently, great strides have been made in predicting volatility in the financial market. However, the so widely used GARCH model suffers from several problems, like the normality assumption which can lead to unreliable estimates and forecasts. In order to solve this problem, it is possible to introduce different new assumptions in the classical GARCH framework. In particular, the GED-GARCH model supposes that the innovation distribution follows a Generalized Error Distribution (GED). In this paper, after a brief theoretical discussion of the statistical model, we show empirically that it is possible to obtain better results using the GED-GARCH(1,1) instead of GARCH(1,1) based on the normality or t-student assumptions.

Much is told in the literature about the determinants that lead graduates to migrate. However, it is crucial to understand how these dynamics have changed after the new worldwide financial crisis: nowadays, inequality has increased, and new generations tend to travel much more than the previous ones, being more prone to look for better opportunities in a different city or country. In this paper, after a brief introduction on the importance of the study and of the various research in the field, we present a Probit regression model to explain the most important determinants of graduates’ migration. We found that life experience, family background and economic factors can explain the willingness to move, and that graduates from southern Italy have an extremely higher probability to move to a different geographical area to look for a job, with respect to Italians of northern regions, while the graduation mark does not lead to significant differences in the probability of migrating.