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The COVID-19 pandemic has demonstrated the increasing need of policymakersfor timely estimates of macroeconomic variables. A prior UNCTAD research paperexamined the suitability of long short-term memory artificial neural networks(LSTM) for performing economic nowcasting of this nature. Here, the LSTM'sperformance during the COVID-19 pandemic is compared and contrasted with thatof the dynamic factor model (DFM), a commonly used methodology in the field.Three separate variables, global merchandise export values and volumes andglobal services exports, were nowcast with actual data vintages and performanceevaluated for the second, third, and fourth quarters of 2020 and the first andsecond quarters of 2021. In terms of both mean absolute error and root meansquare error, the LSTM obtained better performance in two-thirds ofvariable/quarter combinations, as well as displayed more gradual forecastevolutions with more consistent narratives and smaller revisions. Additionally,a methodology to introduce interpretability to LSTMs is introduced and madeavailable in the accompanying nowcast_lstm Python library, which is now alsoavailable in R, MATLAB, and Julia.

Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis