Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The need to combine data from different frequencies plays an important role for many economic decision-makers and economists. The process, which consists in using higher frequency data to construct a higher frequency indicator from its lower frequency counterpart, is called temporal disaggregation. In this paper, we propose a new temporal disaggregation technique based on MIDAS regression using time series data sampled at different frequencies. We first propose a simple disaggregation procedure more flexible than the more traditional approaches, such as Chow–Lin (1971), and we extend the procedure to a dynamic setting. The proposed procedure is flexible enough to take into account seasonality or calendar effects. An extensive simulation study examines the performance of the new approach compared to alternative approaches.