9 citations · 20 across the 11 of their papers we have counts for
14 papers
Model selection confidence sets for time series models with applications to electricity load data
Piersilvio De Bortoli, Davide Ferrari, Francesco Ravazzolo +1
This paper studies the Model Selection Confidence Set (MSCS) methodology for univariate time series models involving autoregressive and moving average components, and applies it to…
Forecasting Oil Consumption: The Statistical Review of World Energy Meets Machine Learning
Jan Ditzen, Erkal Ersoy, Haoyang Li +1
This paper studies whether a small set of dominant countries can account for most of the dynamics of regional oil demand and improve forecasting performance. We focus on dominant d…
Modeling European Electricity Market Integration during turbulent times
Francesco Ravazzolo, Luca Rossini, Andrea Viselli
This paper introduces a novel Bayesian reverse unrestricted mixed-frequency model applied to a panel of nine European electricity markets. Our model analyzes the impact of daily fo…
Dominant Drivers of National Inflation
Jan Ditzen, Francesco Ravazzolo
For western economies a long-forgotten phenomenon is on the horizon: rising inflation rates. We propose a novel approach christened D2ML to identify drivers of national inflation.…
Robust estimation for Threshold Autoregressive Moving-Average models
Greta Goracci, Davide Ferrari, Simone Giannerini +1
Threshold autoregressive moving-average (TARMA) models are popular in time series analysis due to their ability to parsimoniously describe several complex dynamical features. Howev…
Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications
Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of energy commodities, where the homoskedasticity assumption is relaxed to all…
A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources
Fabrizio Durante, Angelica Gianfreda, Francesco Ravazzolo +1
This paper examines the dependence between electricity prices, demand, and renewable energy sources by means of a multivariate copula model {while studying Germany, the widest stud…
Forecasting consumer confidence through semantic network analysis of online news
A. Fronzetti Colladon, F. Grippa, B. Guardabascio +2
This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian med…
Forecasting financial markets with semantic network analysis in the COVID-19 crisis
A. Fronzetti Colladon, S. Grassi, F. Ravazzolo +1
This paper uses a new textual data index for predicting stock market data. The index is applied to a large set of news to evaluate the importance of one or more general economic-re…
Are low frequency macroeconomic variables important for high frequency electricity prices?
Claudia Foroni, Francesco Ravazzolo, Luca Rossini
Recent research finds that forecasting electricity prices is very relevant. In many applications, it might be interesting to predict daily electricity prices by using their own lag…
Proper scoring rules for evaluating asymmetry in density forecasting
Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted ve…
Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration
Angelica Gianfreda, Francesco Ravazzolo, Luca Rossini
This paper compares alternative univariate versus multivariate models, frequentist versus Bayesian autoregressive and vector autoregressive specifications, for hourly day-ahead ele…
Forecaster's Dilemma: Extreme Events and Forecast Evaluation
Sebastian Lerch, Thordis L. Thorarinsdottir, Francesco Ravazzolo +1
In public discussions of the quality of forecasts, attention typically focuses on the predictive performance in cases of extreme events. However, the restriction of conventional fo…
Bayesian Nonparametric Calibration and Combination of Predictive Distributions
Federico Bassetti, Roberto Casarin, Francesco Ravazzolo
We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random ca…