  Large and acute economic shocks such as the 2007-2009 financial crisis and
the current COVID-19 infections rapidly change the economic environment. In
such a situation, the importance of real-time economic analysis using
alternative datais emerging. Alternative data such as search query and location
data are closer to real-time and richer than official statistics that are
typically released once a month in an aggregated form. We take advantage of
spatio-temporal granularity of alternative data and propose a
mixed-FrequencyAggregate Learning (MF-AGL)model that predicts economic
indicators for the smaller areas in real-time. We apply the model for the
real-world problem; prediction of the number of job applicants which is closely
related to the unemployment rates. We find that the proposed model predicts (i)
the regional heterogeneity of the labor market condition and (ii) the rapidly
changing economic status. The model can be applied to various tasks, especially
economic analysis
