CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy

This is the final version. Available from PeerJ via the DOI in this record. Data Availability: The following information was supplied regarding data availability: Data and code are freely available at GitHub: https://github.com/pcm-dpc/COVID-19/tree/master/dati-regioniTo date, official data on the number of people infected with the SARS-CoV-2—responsible for the Covid-19—have been released by the Italian Government just on the basis of a non-representative sample of population which tested positive for the swab. However a reliable estimation of the number of infected, including asymptomatic people, turns out to be crucial in the preparation of operational schemes and to estimate the future number of people, who will require, to different extents, medical attentions. In order to overcome the current data shortcoming, this article proposes a bootstrap-driven, estimation procedure for the number of people infected with the SARS-CoV-2. This method is designed to be robust, automatic and suitable to generate estimations at regional level. Obtained results show that, while official data at March the 12th report 12.839 cases in Italy, people infected with the SARS-CoV-2 could be as high as 105.789

A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordNoise-affected economic time series, realizations of stochastic processes exhibiting complex and possibly nonlinear dynamics, are dealt with. This is often the case of time series found in economics, which notoriously suffer from problems such as low signal-to-noise ratios, asymmetric cycles and multiregimes patterns. In such a framework, even sophisticated statistical models might generate suboptimal predictions, whose quality can further deteriorate unless time consuming updating or deeper model revision procedures are carried out on a regular basis. However, when the models' outcomes are expected to be disseminated in timeliness manner (as in the case of Central Banks or national statistical offices), their modification might not be a viable solution, due to time constraints. On the other hand, if the application of simpler linear models usually entails relatively easier tuning-up procedures, this would come at the expenses of the quality of the predictions yielded. A mixed, self-tuning forecasting method is therefore proposed. This is an automatic, 2-stage procedure, able to generate predictions by exploiting the denoising capabilities provided by the wavelet theory in conjunction with a compounded forecasting generator. Its out-of-sample performances are evaluated through an empirical study carried out on macroeconomic time series

Forecasting the COVID-19 diffusion in Italy and the related occupancy of intensive care units

This is the final version. Available from Hindawi via the DOI in this record. Data Availability: The data used to support the findings of this study are available from the corresponding author upon request.This paper provides a model-based method for the forecast of the total number of currently COVID-19 positive individuals and of the occupancy of the available intensive care units in Italy. The predictions obtained—for a time horizon of 10 days starting from March 29th—will be provided at a national as well as at a more disaggregated level, following a criterion based on the magnitude of the phenomenon. While those regions hit the most by the pandemic have been kept separated, those less affected regions have been aggregated into homogeneous macroareas. Results show that—within the forecast period considered (March 29th–April 7th)—all of the Italian regions will show a decreasing number of COVID-19 positive people. The same will be observed for the number of people who will need to be hospitalized in an intensive care unit. These estimates are valid under constancy of the government’s current containment policies. In this scenario, northern regions will remain the most affected ones, whereas no significant outbreaks are foreseen in the southern regions

Smoothing parameter estimation for first order discrete time infinite impulse response filters

This is the final version. Available from MedCrave Group via the DOI in this record. Discrete time Infinite Impulse Response low-pass filters are widely used in many fields such as engineering, physics and economics. Once applied to a given time series, they have the ability to pass low frequencies and attenuate high frequencies. As a result, the data are expected to be less noisy. A properly filtered signal, is generally more informative with positive repercussions involving qualitative aspects – e.g. visual inspection and interpretation – as well as quantitative ones, such as its digital processing and mathematical modelling. In order to effectively disentangle signal and noise, the filter smoothing constant, which controls the degree of smoothness in First Order Discrete Time Infinite Impulse Response Filters, has to be carefully selected. The proposed method conditions the estimation of the smoothing parameter to a modified version of the information criterion of the type Hannan - Quinn which in turns is built using the Estimated Log Likelihood Function of a model of the class SARIMA (Seasonal Auto Regressive Moving Average). Theoretical evidences as well as an empirical study conducted on a particularly noisy time series will be presented

Time series chaos detection and assessment via scale dependent Lyapunov Exponent

This is the final version. Available from the Canadian Center of Science and Education via the DOI in this record. Many dynamical systems in a wide range of disciplines -- such as engineering, economy and biology -- exhibit complex behaviors generated by   nonlinear components which might result in deterministic chaos. While in  lab--controlled setups its detection and level estimation  is in general a doable task, usually the same  does not hold for many   practical applications. This is because experimental conditions imply facts like low signal--to--noise ratios, small sample sizes and not--repeatability  of the experiment, so that the performances of the tools commonly employed for chaos detection can be seriously affected.  To tackle this problem, a combined approach based on wavelet and chaos theory is proposed. This is a procedure designed to provide the analyst with qualitative and quantitative information, hopefully conducive to a better understanding of the dynamical system the time series under investigation is generated from. The chaos detector considered is the well known Lyapunov Exponent. A real life application, using the Italian Electric Market price index, is employed to corroborate the validity of the proposed approach.</jats:p

Loss of fitting and distance prediction in fixed vs updated ARIMA models

This is the final version. Available from Global Journals via the DOI in this record. In many cases, it might be advisable to keep an operational time series model fixed for a given span of time, instead of updating it as a new datum becomes available. One common case, is represented by model–based deseasonalization procedures, whose time series models are updated on a regular basis by National Statistical Offices. In fact, in order to minimize the extent of the revisions and grant a greater stability of the already released figures, the interval in between two updating processes is kept "reasonably" long (e.g. one year). Other cases can be found in many contexts, e.g. in engineering for structural reliability analysis or in all those cases where model re–estimation is not a practical or even a viable options, e.g. due to time constraints or computational issues. Clearly, the inevitable trade–off between a fixed models and its updated counterpart, e.g. in terms of fitting performances, out–of–sample prediction capabilities or dynamics explanation should be always accounted for

Bootstrap order determination for ARMA models: a comparison between different model selection criteria

This is the final version. Available from Hindawi via the DOI in this record. The present paper deals with the order selection of models of the class for autoregressive moving average. A novel method—previously designed to enhance the selection capabilities of the Akaike Information Criterion and successfully tested—is now extended to the other three popular selectors commonly used by both theoretical statisticians and practitioners. They are the final prediction error, the Bayesian information criterion, and the Hannan-Quinn information criterion which are employed in conjunction with a semiparametric bootstrap scheme of the type sieve

Forecasting composite indicators with anticipated information: an application to the industrial production index

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordMany economic and social phenomena are measured by composite indicators computed as weighted averages of a set of elementary time series. Often data are collected by means of large sample surveys, and processing takes a long time, whereas the values of some elementary component series may be available some time before the others, and may be used for forecasting the composite index. This problem is addressed within the framework of prediction theory for stochastic processes. A method is proposed for exploiting anticipated information in order to minimise the mean square forecast error, and for selecting the most useful elementary series. An application to the Italian general industrial production index is illustrated, which demonstrates that knowledge of anticipated values of some, or even just one, component series may reduce the forecast error considerably.Ministero della Istruzione, ItalyUniversit´a e Ricerca Scientifica, ItalyConsiglio Nazionale delle Ricerche, Ital

Predictive capacity of COVID-19 test positivity rate.

This is the final version. Available from MDPI via the DOI in this record. Data Availability Statement: The data used in this paper are made available by the Italian Civil Protection Department and publicly accessible, free of charge, at the following web address: https://github.com/pcm-dpc (accessed on 11 February 2021). The TPR and hospitalized time series are available at the following web address: http://www.cs.unibo.it/~gaspari/www/italy.html (accessed on 11 February 2021)COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy

Forecasting youth unemployment in the aftermath of the COVID-19 pandemic: the Italian case

This is the author accepted manuscript. The final version is available from Amanxo Publication via the DOI in this record Purpose: This study aims at forecasting NEET unemployment in Italy using a counterfactual scenario, based on an original empirical model, whereby the effects of the COVID-19 pandemic on the NEET rate are factored in and left out. Methodology: An artificial neural network (ANN) model of the type feed-forward, with a Google Trends-generated variable that represents potentially relevant search queries, is employed to backcast, nowcast and forecast Italian NEET unemployment for 2019, 2020, 2021, respectively. Findings: Findings suggest that the Italian NEET unemployment rate will slightly increase in a less than proportional way, absorbing the COVID-19 pandemic’s effects in a relatively short time period. Research Implications/ Limitation: Several limitations with respect to the limited sample size and the few number of explanatory variables are remedied through the use of an adequate methodology. Originality: The use of an ANN in youth unemployment studies during a pandemic of the present scale is, to the best of the authors’ knowledge, unprecedented