  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.
