My research interests are in the fields of Forecasting, Empirical Finance and Applied Econometrics.
Lately I have been digging into Big Data Econometrics and High-dimensional econometrics and issues related to applications of Machine Learning to economics and econometrics.
In thise page you can find some of my worksing papers and work in progress.
I am currently working on extracting indicators from social media and other big data sources that might be useful for policy questions.
Del Monaco, Andrea, and Longo, Luigi, and Marcucci, Juri, and Tafani, Irene, (2025), "Reddit's Pulse on US Inflation: Forecasting with Large Language Models'', Bank of Italy, working paper.
Abstract
This paper employs Large Language Models (LLMs) to extract meaningful signals from Reddit's social media data and forecast U.S. inflation. We focus on popular economics-related subreddits, constructing indexes that capture users' interests and perceptions of inflation and price dynamics. To derive relevant information from social media conversations, we fine-tune pre-trained LLMs (including BERT, Qwen, LLaMA or Gemma) using labels generated by human annotators and ChatGPT. Our results demonstrate that indicators derived from fine-tuned LLMs significantly improve the time series models' point and density forecasting performance for U.S. inflation.
Bonfanti, Giovanni, and Marcucci, Juri, (2025), "A European Safe Asset? Not Without the Investors'', Bank of Italy, working paper.
Abstract
We study bonds issued by the European Union as a common liability of the member countries through various supranational institutions. We show that this debt pays an interest rate that is higher than for comparably safe, large, and liquid sovereign issuers. This spread widens during risk-off movements and varies with expectations about monetary policy. We develop a model where investment mandates linked to the inclusion in fixed-income indices reduce the size of potential investors in EU bonds relative to equally safe governments. The smaller size of potential buyers translates into lower liquidity during crises and hence EU bonds must pay a premium even during normal times. This mechanism is driven by investors with potential liquidity needs during crises such as foreign reserve managers that do not see EU bonds as substitutes for highly-rated sovereigns. Expectations about state-contingent purchases by the ECB can significantly compress this premium even if they are not targeted to EU bonds.
Bacco, Luca, and Laureti, Tiziana, and Marcucci, Juri, and Palumbo, Luigi, and Sasso, Daniele, and Vollero, Luca, (2025), "Nowcasting the Italian Consumer Price Index Using Online Prices and Machine Learning'', Bank of Italy, working paper.
Abstract
Timely and accurate forecasts of the Consumer Price Index (CPI), an essential economic indicator measuring consumer prices over time, are crucial for central banks. Traditional forecasting models often struggle to incorporate real-time data and adapt to rapid changes in the economic environment, leading to potential inaccuracies in short-term forecasts. In this paper, we explore the potential of using online food price data obtained from 20 supermarkets across several major cities of a well-known chain in Italy from December 2020 to March 2023. Our objective is exploring the feasibility and accuracy of forecasting CPI for specific food categories using real-time, web-scraped data, particularly in periods of high macroeconomic uncertainty like those following the COVID-19 pandemic and the onset of the war in Ukraine. Our analysis demonstrates the potential of real-time web-scraped data for predicting official CPIs and offers valuable insights for researchers and practitioners interested in this specific approach. In particular, our results suggest that web-based price data can complement traditional statistical sources, providing more granular and timely indicators that are especially useful during periods of economic volatility.
Old Working Papers
Marcucci, Juri, and Mistrulli, Paolo Emilio, (2013), "Women Entrepreneurs in bad shape: Is the duration of their bad loans more persistent?''.
Abstract
We analyze the duration of bad loans for a unique data set of sole proprietorships in Italy, finding that the duration of bad loans for female firms is longer. However, this is mainly due to the fact that female firms' loans are less frequently written off compared to male ones, suggesting that female firms might be more creditworthy than male firms. These findings are robust to censoring, alternative specifications of the distribution of bad loan duration and other bank-specific control variables.
D'Amuri, Francesco and Marcucci, Juri, (2012), "The predictive power of Google searches in forecasting unemployment", Bank of Italy Working paper (Tema di discussione) n. 891 (Published on the International Journal of Forecasting).
Abstract
We suggest the use of an index of Internet job-search intensity (the Google Index, GI) as the best leading indicator to predict the US monthly unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt our preferred leading indicator (GI), the more standard initial claims or combinations of both. We find that models augmented with the GI outperform the traditional ones in predicting the unemployment rate for different out-of-sample intervals that start before, during and after the Great Recession. Google-based models also outperform standard ones in most state-level forecasts and in comparison with the Survey of Professional Forecasters. These results survive a falsification test and are also confirmed when employing different keywords. Based on our results for the unemployment rate, we believe that there will be an increasing number of applications using Google query data in other fields of economics.
D'Amuri, Francesco and Marcucci, Juri, (2009), "Google it! Forecasting the US unemployment rate with a Google job search index", MPRA working paper n. 18248, University Library of Munich, Germany and ISER working paper n. 2009/32 (Completely revised and published in the IJF with a new title).
Abstract
We suggest the use of an Internet job-search indicator (the Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. We find that models augmented with the GI outperform the traditional ones in predicting the monthly unemployment rate, even in most state-level forecasts and in comparison with the Survey of Professional Forecasters.
Ardizzi, Guerino, Emiliozzi, Simone, Marcucci, Juri, Monteforte, Libero, (2019), "News and Consumer Card Payments", Bank of Italy Working paper
Bruno Giuseppe, Cerchiello Paola, Marcucci Juri, Nicola Giancarlo, (2018), "Twitter Sentiment and Banks' Financial Ratios: Is There A Casual Link?", Bank of Italy, mimeo
Bruno Giuseppe, Marcucci Juri, Mattiocco Attilio, Scarnò Marco, Sforzini Donatella, (2018), "The Sentiment Hidden in Italian Texts Through the Lens of a New Dictionary", Bank of Italy, mimeo
Francesco Billari, Francesco D'Amuri, and Juri Marcucci (2018), "Forecasting US Birth Rates Using Google Trends", Bank of Italy, mimeo.