  This paper demonstrates the potentials of the long short-term memory (LSTM)
when applyingwith macroeconomic time series data sampled at different
frequencies. We first present how theconventional LSTM model can be adapted to
the time series observed at mixed frequencies when thesame mismatch ratio is
applied for all pairs of low-frequency output and higher-frequency variable.
Togeneralize the LSTM to the case of multiple mismatch ratios, we adopt the
unrestricted Mixed DAtaSampling (U-MIDAS) scheme (Foroni et al., 2015) into the
LSTM architecture. We assess via bothMonte Carlo simulations and empirical
application the out-of-sample predictive performance. Ourproposed models
outperform the restricted MIDAS model even in a set up favorable to the
MIDASestimator. For real world application, we study forecasting a quarterly
growth rate of Thai realGDP using a vast array of macroeconomic indicators both
quarterly and monthly. Our LSTM withU-MIDAS scheme easily beats the simple
benchmark AR(1) model at all horizons, but outperformsthe strong benchmark
univariate LSTM only at one and six months ahead. Nonetheless, we find thatour
proposed model could be very helpful in the period of large economic downturns
for short-termforecast. Simulation and empirical results seem to support the
use of our proposed LSTM withU-MIDAS scheme to nowcasting application.
