Nowcasting the State of the Italian Economy: The Role of Financial Markets

Abstract

This paper compares several methods for constructing weekly nowcasts of recession probabilities in Italy, with a focus on the most recent period of the Covid-19 pandemic. The common thread of these methods is that they use, in different ways, the information content provided by financial market data. In particular, a battery of probit models are estimated after extracting information from a large dataset of more than 130 financial market variables observed at a weekly frequency. The predictive accuracy of these models is explored in a pseudo out-of-sample forecasting exercise. The results demonstrate that nowcasts derived from probit models estimated on a large set of financial variables are, on average, more accurate than standard probit models estimated on a single financial covariate, such as the slope of the yield curve. The proposed approach performs well even compared with probit models estimated on single time series of real economic activity, such as industrial production, or on composite PMI indicators. Overall, the financial indicators used in this paper can be easily updated as soon as new data become available on a weekly basis, thus providing a reliable real-time dating of the Italian business cycle.

Keywords: financial markets, probit models, factor-augmented probit models, model confidence set, penalized likelihood, forecast evaluation

JEL Classification: C22, C25, C53, E32