Econometric analyses with backdated data: unified Germany and the euro area

In this paper we compare alternative approaches for the construction of time series of macroeconomic variables for Unified Germany prior to 1991, and then use them for the construction of corresponding time series for the euro area. The resulting series for Germany and the euro area are compared with existing ones on the basis of both descriptive statistics and results of econometric analyses conducted with the alternative time series. We find that more sophisticated time series methods for backdating can yield sizeable gains. JEL Classification: C32, C43, C82Backdating, euro area, factor model, Unified Germany

Interpolation and backdating with a large information set

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are fastly estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all the available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is more robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, depending on the type of interpolation but again be reduced with multivariate approaches, including factor-based ones. JEL Classification: C32, C43, C82factor model, Interpolation, Kalman filter, spline

Overconfidence in the art market: a bargaining pricing model with asymmetric disinformation

This paper develops a Nash bargaining model of price formation in the art market. Agents can be naïve, if they are overconfident and either overestimate artistic quality or underestimate their uncertainty of artistic quality, or sophisticated, if they correctly use all the available information. Overconfidence turns out to have a positive impact on both the price and the average quality of the artworks traded in the market. The impact of overconfidence on expected quality is weaker than the corresponding price increase, so sellers overcharge buyers. In addition, the buyer’s (seller’s) overconfidence has a positive (negative) impact on the likelihood of trade. If many pairs of agents may bargain simultaneously, we find that seller’s market power is negatively affected by the number of sellers and positively affected by the number of buyers. If sophisticated and naïve buyers coexist, naïve buyers exert a negative externality on the sophisticated ones, increasing the price the latter pay

Rough volatility via the Lamperti transform

We study the roughness of the log-volatility process by testing the self-similarity of the process obtained by the de-Lampertized realized volatility. The value added of our analysis rests on the application of a distribution-based estimator providing results which are more robust with respect to those deduced by the scaling of the individual moments of the process. Our findings confirm the roughness of the log-volatility process

Italian National Vegetation Database (BVN/ISPRA)

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Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population : study protocol

Purpose Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device. Methods This study will enrol T1D paediatric participants who already use CGM. Participants will wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices. Results Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm. Conclusion This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions

Subtle changes in central dopaminergic tone underlie bradykinesia in essential tremor

Introduction: In this research, our primary objective was to explore the correlation between basal ganglia dopaminergic neurotransmission, assessed using 123I-FP-CIT (DAT-SPECT), and finger movements abnormalities in patients with essential tremor (ET) and Parkinson's disease (PD). Methods: We enrolled 16 patients with ET, 17 with PD, and 18 healthy controls (HC). Each participant underwent comprehensive clinical evaluations, kinematic assessments of finger tapping. ET and PD patients underwent DAT-SPECT imaging. The DAT-SPECT scans were subjected to both visual and semi-quantitative analysis using DaTQUANT®. We then investigated the correlations between the clinical, kinematic, and DAT-SPECT data, in patients. Results: Our findings confirm that individuals with ET exhibited slower finger tapping than HC. Visual evaluation of radiotracer uptake in both striata demonstrated normal levels within the ET patient cohort, while PD patients displayed reduced uptake. However, there was notable heterogeneity in the quantification of uptake within the striata among ET patients. Additionally, we found a correlation between the amount of radiotracer uptake in the striatum and movement velocity during finger tapping in patients. Specifically, lower radioligand uptake corresponded to decreased movement velocity (ET: coef. = 0.53, p-adj = 0.03; PD: coef. = 0.59, p-adj = 0.01). Conclusion: The study's findings suggest a potential link between subtle changes in central dopaminergic tone and altered voluntary movement execution, in ET. These results provide further insights into the pathophysiology of ET. However, longitudinal studies are essential to determine whether the slight reduction in dopaminergic tone observed in ET patients represents a distinct subtype of the disease or could serve as a predictor for the clinical progression into PD

A self-attention deep neural network regressor for real time blood glucose estimation in paediatric population using physiological signals

With the advent of modern digital technology, the physiological signals (such as electrocardiogram) are being acquired from portable wearable devices which are being used for non-invasive chronic disease management (such as Type 1 Diabetes). The diabetes management requires real-time assessment of blood glucose which is cumbersome for paediatric population due to clinical complexity and invasiveness. Therefore, real-time non-invasive blood glucose estimation is now pivotal for effective diabetes management. In this paper, we propose a Self-Attention Deep Neural Network Regressor for real-time non-invasive blood glucose estimation for paediatric population based on automatically extracted beat morphology. The first stage performs Morphological Extractor based on Self-Attention based Long Short-Term Memory driven by Convolutional Neural Network for highlighting local features based on temporal context. The second stage is based on Morphological Regressor driven by multilayer perceptron with dropout and batch normalization to avoid overfitting. We performed feature selection via logit model followed by Spearman’s correlation among features to avoid feature redundancy. We trained as tested our model on publicly available MIT/BIH-Physionet databases and physiological signals acquired from a T1D paediatric population