Comparing density forecasts of aggregated time series via bootstrap

When forecasting aggregated time series, several options are available. For example, the multivariate series or the individual time series might be predicted and then aggregated, or one may choose to forecast the aggregated series directly. While in theory an optimal disaggregated forecast will generally be superior (or at least not inferior) to forecasts based on aggregated information, this is not necessarily true in practical situations. The main reason is that the true data generating process is usually unknown and models need to be specified and estimated on the basis of the available information. This paper describes a bootstrap-based procedure, in the context of vector autoregressive models, for ranking the different forecasting approaches for contemporaneous time series aggregates. Uncertainty due to parameter estimation will be considered and the ranking will be based not only on the mean squared forecast error, but more in general on the performance of the forecast distribution. The forecasting procedures are applied to the United States aggregate inflation

A new time-varying model for forecasting long-memory series

In this work we propose a new class of long-memory models with time-varying fractional parameter. In particular, the dynamics of the long-memory coefficient, $d$, is specified through a stochastic recurrence equation driven by the score of the predictive likelihood, as suggested by Creal et al. (2013) and Harvey (2013). We demonstrate the validity of the proposed model by a Monte Carlo experiment and an application to two real time series

Value-at-Risk prediction by higher moment dynamics.

In this paper the prediction of Value-at-Risk by means of models accounting for higher moment dynamics is studied. We consider the GARCHDSK model, which allows for dynamic skewness and kurtosis, and compare its performance with that of several widely adopted models. The analysis is based on the study of sequences of (long and short) VaR violations, for which the hypotheses of absence of autocorrelation and of correct coverage rates are assessed. Both in-sample and out-of-sample results are investigated

Looking for skewness in financial time series

In this paper marginal and conditional skewness of financial return time series is studied, by means of testing procedures and suitable models, for nine major international stock indexes. To analyze time-varying conditional skewness a new GARCH-type model with dynamic skewness and kurtosis is proposed. Results indicate that there are no evidences of marginal asymmetry in the nine series, but there are clear findings of significant time-varying conditional skewness. The economic significance of conditional skewness is analyzed and compared by considering Value-at-Risk, Expected Shortfall and Market Risk Capital Requirements set by the Basel Accord

Censorship in democracy

The spread of propaganda, misinformation, and biased narratives from autocratic regimes, especially on social media, is a growing concern in many democracies. Can censorship be an effective tool to curb the spread of such slanted narratives? In this paper, we study the European Union’s ban on Russian state-led news outlets after the 2022 Russian invasion of Ukraine. We analyze 775,616 tweets from 133,276 users on Twitter/X, employing a difference-in-differences strategy. We show that the ban reduced pro-Russian slant among users who had previously directly interacted with banned outlets. The impact is most pronounced among users with the highest pre-ban slant levels. However, this effect was short-lived, with slant returning to its pre-ban levels within two weeks post-enforcement. Additionally, we find a detectable albeit less pronounced indirect effect on users who had not directly interacted with the outlets before the ban. We provide evidence that other suppliers of propaganda may have actively sought to mitigate the ban’s influence by intensifying their activity, effectively counteracting the persistence and reach of the ban

Mind-muscle connection: effects of verbal instructions on muscle activity during bench press exercise

Different attentional foci may modify muscle activation during exercises. Our aim was to determine if it is possible to selectively activate the pectoralis major or triceps brachii muscles according to specific verbal instructions provided during the bench press exercise. 13 resistance-trained males (25.6\ub15.4 yrs, 182.7\ub19.1 cm, 86.4\ub19.7 kg) underwent an electromyographic signals acquisition of the sternocostal head, clavicular head of the pectoralis major, the anterior deltoid, and the long head of the triceps brachii (LT) during bench press exercise. Participants performed one non-instructed set (NIS) of 4 repetitions at 50% 1-repetition maximum (1-RM) and one NIS of 4 repetitions at 80% 1-RM. Four additional sets of 4 repetitions at 50% and 80% 1-RM were randomly performed with verbal instructions to isolate the chest muscles (chest instructed set, CIS) or to isolate the triceps muscles (triceps instructed set, TIS). Participants showed significantly higher LT activation during TIS compared to non-instructed set both at 50% (p=0.0199) and 80% 1-RM (p=0.0061) respectively. TIS elicited a significant (p=0.0250) higher activation of LT compared to CIS. Our results suggest that verbal instructions seem to be effective for increasing activity of the triceps brachii but not the pectoralis major during the bench press

Mind-muscle connection: effects of verbal instructions on muscle activity during bench press exercise

Different attentional foci may modify muscle activation during exercises. Our aim was to determine if it is possible to selectively activate the pectoralis major or triceps brachii muscles according to specific verbal instructions provided during the bench press exercise. 13 resistance-trained males (25.6±5.4 yrs, 182.7±9.1 cm, 86.4±9.7 kg) underwent an electromyographic signals acquisition of the sternocostal head, clavicular head of the pectoralis major, the anterior deltoid, and the long head of the triceps brachii (LT) during bench press exercise. Participants performed one non-instructed set (NIS) of 4 repetitions at 50% 1-repetition maximum (1-RM) and one NIS of 4 repetitions at 80% 1-RM. Four additional sets of 4 repetitions at 50% and 80% 1-RM were randomly performed with verbal instructions to isolate the chest muscles (chest instructed set, CIS) or to isolate the triceps muscles (triceps instructed set, TIS). Participants showed significantly higher LT activation during TIS compared to non-instructed set both at 50% (p=0.0199) and 80% 1-RM (p=0.0061) respectively. TIS elicited a significant (p=0.0250) higher activation of LT compared to CIS. Our results suggest that verbal instructions seem to be effective for increasing activity of the triceps brachii but not the pectoralis major during the bench press

Comparing density forecasts of aggregated time series via bootstrap

When forecasting aggregated time series, several options are available. For example, the multivariate series or the individual time series might be predicted and then aggregated, or one may choose to forecast the aggregated series directly. While in theory an optimal disaggregated forecast will generally be superior (or at least not inferior) to forecasts based on aggregated information, this is not necessarily true in practical situations. The main reason is that the true data generating process is usually unknown and models need to be specified and estimated on the basis of the available information. This paper describes a bootstrap-based procedure, in the context of vector autoregressive models, for ranking the different forecasting approaches for contemporaneous time series aggregates. Uncertainty due to parameter estimation will be considered and the ranking will be based not only on the mean squared forecast error, but more in general on the performance of the forecast distribution. The forecasting procedures are applied to the United States aggregate inflation

Value-at-Risk prediction by higher moment dynamics.

In this paper the prediction of Value-at-Risk by means of models accounting for higher moment dynamics is studied. We consider the GARCHDSK model, which allows for dynamic skewness and kurtosis, and compare its performance with that of several widely adopted models. The analysis is based on the study of sequences of (long and short) VaR violations, for which the hypotheses of absence of autocorrelation and of correct coverage rates are assessed. Both in-sample and out-of-sample results are investigated