Nowcasting inflation with Lasso-regularized vector autoregressions and mixed frequency data

We evaluate the predictive performances of the least absolute shrinkage and selection operator (Lasso) as an alternative shrinkage method for high-dimensional vector auto-regressions. The analysis extends the Lasso-based multiple equations regularization to a mixed/high-frequency data setting. Very short-term forecasting (nowcasting) is used to target the Euro area's inflation rate. We show that this approach can outperform more standard nowcasting tools in the literature, producing nowcasts that closely follow actual data movements. The proposed tool can overcome information and policy decision problems related to the substantial publishing delays of macroeconomic aggregates

Firms Default Prediction with Machine Learning

Academics and practitioners have studied over the years models for predicting firms bankruptcy, using statistical and machine-learning approaches. An earlier sign that a company has financial difficulties and may eventually bankrupt is going in default, which, loosely speaking means that the company has been having difficulties in repaying its loans towards the banking system. Firms default status is not technically a failure but is very relevant for bank lending policies and often anticipates the failure of the company. Our study uses, for the first time according to our knowledge, a very large database of granular credit data from the Italian Central Credit Register of Bank of Italy that contain information on all Italian companies’ past behavior towards the entire Italian banking system to predict their default using machine-learning techniques. Furthermore, we combine these data with other information regarding companies’ public balance sheet data. We find that ensemble techniques and random forest provide the best results, corroborating the findings of Barboza et al. (Expert Syst. Appl., 2017)

Overcoming Multi Finger Turn-On In Hv Diacs Using Local Polv-Ballasting

Miscorrelation between TLP. IEC contact and IEC air-gap high current capability of dual- direction DIAC devices is experimentally observed and explained by non-simultaneous turn on the DIAC device fingers. The effect is studied using numerical simulation, followed by the experimental validation of the new proposed design with poly-emitter ballasting