Forecasting innovative start-ups through automatic variable selection and MIDAS regressions

Abstract

ABSTRACT The paper focuses on the role that both macroeconomic indicators and financial factors play as drivers for the birth and the evolution of the Italian innovative start-ups over time. The analysis makes use of a novel machine learning algorithm, working in high-dimensional graphical models, to select, among the multitude of potential determinants, the relevant explanatory variables for the number of innovative start-ups. Being the variables involved in the analysis sampled at different frequencies, mixed data sampling models are employed for estimation and forecasting purposes. The need to reduce the number of regressors of these models, for reasons related to their estimability, is met by resorting to principal component analysis of the potential determinants. An empirical application to the set of Italian innovative start-ups, either in their entirety or split according to some specific requirements, highlights the effectiveness of this approach. The analysis results provides useful information for the assessment of national innovation policies and for the evaluation of the capability of the innovative start-ups to persist and contribute at the recovery during the actual crisis still affected by the pandemic.