A Machine Learning Approach to Construct Quarterly Data on Intangible Investment for Eurozone

We develop a novel approach to construct quarterly time series data for annually measured intangible investment variables. We accomplish this by using machine learning methods to explore the relationship between these variables and key macroeconomic time series available on a quarterly frequency. The proposed approach offers some advantages over other econometric techniques. Specifically, it does not not require any ex-ante assumptions for the link between the quarterly time series and their annual counterparts, and it is free from issues such as multicollinearity and endogeneity, requiring almost no data pre-processing. To demonstrate the usefulness of the constructed data, we present some bysiness cycle facts for the intangible economies of Eurozone and estimate a dynamic factor model