Research
Research
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.
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.
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?", in progress
Bruno Giuseppe, Marcucci Juri, Mattiocco Attilio, Scarnò Marco, Sforzini Donatella, (2018), "The Sentiment Hidden in Italian Texts Through the Lens of a New Dictionary", in progress
Francesco Billari, Francesco D'Amuri, and Juri Marcucci (2018), "Forecasting US Birth Rates Using Google Trends", in progress.