Nowcasting R&D Expenditures: A Machine Learning Approach

TL;DR

This paper introduces a neural-network-based nowcasting framework to predict and interpolate high-frequency R&D expenditure data from infrequent surveys, leveraging mixed-frequency data like Internet search volumes for improved accuracy.

Contribution

It presents a novel two-step machine learning approach combining neural networks and elasticities to nowcast and interpolate R&D expenditures from mixed-frequency data.

Findings

The model outperforms classical regression and sparse disaggregation methods.

High relevance of Internet search data in improving predictions.

Monthly R&D expenditure estimates strongly correlate with employment data.

Abstract

Macroeconomic data are crucial for monitoring countries' performance and driving policy. However, traditional data acquisition processes are slow, subject to delays, and performed at a low frequency. We address this 'ragged-edge' problem with a two-step framework. The first step is a supervised learning model predicting observed low-frequency figures. We propose a neural-network-based nowcasting model that exploits mixed-frequency, high-dimensional data. The second step uses the elasticities derived from the previous step to interpolate unobserved high-frequency figures. We apply our method to nowcast countries' yearly research and development (R&D) expenditure series. These series are collected through infrequent surveys, making them ideal candidates for this task. We exploit a range of predictors, chiefly Internet search volume data, and document the relevance of these data in improving out-of-sample predictions. Furthermore, we leverage the high frequency of our data to derive monthly estimates of R&D expenditures, which are currently unobserved. We compare our results with those obtained from the classical regression-based and the sparse temporal disaggregation methods. Finally, we validate our results by reporting a strong correlation with monthly R&D employment data.