Revisiting the empirical evidence on firmsÂ’ money demand

In this paper we estimate the demand for liquidity by US non financial firms using data from COMPUSTAT database. In contrast to the previous literature, we consider firm-specific effects, such as cost-of-capital and wages. From the balanced and unbalanced panel estimations we infer that there are economies of scale in money demand by US business firms, because estimated sales elasticities are smaller than unity. In particular, they are lower than in previous empirical studies, suggesting that economies of scale in the demand for money are even bigger than formerly thought. In addition, it emerges that labor is not a substitute for money.Panel Data, Liquidity, Demand for Money, COMPUSTAT

Is Bank Portfolio Riskiness Procyclical? Evidence from Italy using a Vector Autoregression

This study analyzes the cyclical behaviour of the default rates of Italian bank borrowers over the last two decades. A vector autoregression (VAR) modelling technique is employed to assess the extent to which macroeconomic shocks affect the banking sector (first round effect). The VAR also helps to disentangle the feedback effects from the financial system to the real side of the economy. We find evidence of the first round effect and some support for the feedback effect which operates via the bank capital channel.First-round effect; procyclicality; feedback effects; VAR; banks; default rate

Credit risk and business cycle over different regimes

In the recent banking literature on the relationship between credit risk and the business cycle, the presence of asymmetric effects both across credit risk regimes and through the business cycle has been generally neglected. Employing threshold regression models both at the aggregate and the bank level and exploiting a unique dataset on Italian bank borrowersÂ’ default rates, this paper analyzes whether this relationship is characterized by regime switches and thus by asymmetries, determining the thresholds endogenously. Our results show that not only are the effects of the business cycle on credit risk more pronounced during downturns but also when credit risk conditions are poor.Credit Risk, Panel Threshold Regression Models, Regime Switching, Default Rate, Business Cycle, Cyclicality, Basel 2

Forecasting Births Using Google

Abstract de la ponencia[EN] Monitoring fertility change is particularly important for policy and planning purposes. New data may help us in this monitoring. We propose a new leading indicator based on Google web-searches. We then test its predictive power using US data. In a deep out-of sample comparison we show that popular time series specifications augmented with web-search-related data improve their forecasting performance at forecast horizons of 6 to 24 months. The superior performance of these augmented models is confirmed by formal tests of equal forecast accuracy. Moreover, our results survive a falsification test and are confirmed also when a forecast horse race is conducted using different out-of-sample tests, and at the state rather than at the federal level. Conditioning on the same information set, the forecast error of our best model for predicting 2009 births is 35% lower than the Census bureau projections. Our findings indicate the potential use of Googe web-searches in monitoring fertility change and in informing fertility forecasts.Billari, F.; D'amuri, F.; Marcucci, J. (2016). Forecasting Births Using Google. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 119-119. https://doi.org/10.4995/CARMA2016.2015.4301OCS11911

Textual analysis of a Twitter corpus during the COVID-19 pandemics

[EN] Text data gathered from social media are extremely up-to-date and have a great potential value for economic research. At the same time, they pose some challenges, as they require different statistical methods from the ones used for traditional data. The aim of this paper is to give a critical overview of three of the most common techniques used to extract information from text data: topic modelling, word embedding and sentiment analysis. We apply these methodologies to data collected from Twitter during the COVID-19 pandemic to investigate the influence the pandemic had on the Italian Twitter community and to discover the topics most actively discussed on the platform. Using these techniques of automated textual analysis, we are able to make inferences about the most important subjects covered over time and build real-time daily indicators of the sentiment expressed on this platform.Astuti, V.; Crispino, M.; Langiulli, M.; Marcucci, J. (2022). Textual analysis of a Twitter corpus during the COVID-19 pandemics. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 276-276. http://hdl.handle.net/10251/18975927627

The Sentiment Hidden in Italian Texts Through the Lens of A New Dictionary

Resumen de la comunicación[EN] The aim of this work is to propose a strategy to classify texts (or parts of them) in an ordinal emotional scale to gauge a sentiment indicator in every domain. In particular, we develop a new dictionary for the Italian language which is built using an objective method where the polarities of synonyms and antonyms are accounted for in an iterative process. To build our sentiment indicator negations and intensifiers are also used, thus considering the context in which the single word is written. We apply our new dictionary to extract the sentiment from a set of around 40 issues of the Bank of Italy quarterly Economic Bulletin. Our results show that our strategy is able to correctly identify the sentiment expressed in the Bulletins, which is correlated to the main macroeconomic variables (such as national GDP, investment, consumption or unemployment rate). Our analysis shows that sentiment represents not only an evaluation of the stylistic way in which texts are written, but also a valid synthesis of all the external factors analysed in the same document.Bruno, G.; Marcucci, J.; Mattiocco, A.; Scarnò, M.; Sforzini, D. (2018). The Sentiment Hidden in Italian Texts Through the Lens of A New Dictionary. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 265-265. https://doi.org/10.4995/CARMA2018.2018.858026526

"Google it!" Forecasting the US unemployment rate with a Google job search index

In this paper we suggest the use of an internet job-search indicator (Google Index, GI) as the best leading indicator to predict the US unemployment rate. We perform a deep out-of-sample comparison of many forecasting models. With respect to the previous literature we concentrate on the monthly series extending the out-of-sample forecast comparison with models that adopt both our preferred leading indicator (GI), the more standard initial claims or combinations of both. Our results show that the GI indeed helps in predicting the US unemployment rate even after controlling for the effects of data snooping. Robustness checks show that models augmented with the GI perform better than traditional ones even in most state-level forecasts and in comparison with the Survey of Professional Forecasters' federal level predictions

“Google it!” Forecasting the US Unemployment Rate with a Google Job Search index

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