  Nowcasting can play a key role in giving policymakers timelier insight to
data published with a significant time lag, such as final GDP figures.
Currently, there are a plethora of methodologies and approaches for
practitioners to choose from. However, there lacks a comprehensive comparison
of these disparate approaches in terms of predictive performance and
characteristics. This paper addresses that deficiency by examining the
performance of 12 different methodologies in nowcasting US quarterly GDP
growth, including all the methods most commonly employed in nowcasting, as well
as some of the most popular traditional machine learning approaches.
Performance was assessed on three different tumultuous periods in US economic
history: the early 1980s recession, the 2008 financial crisis, and the COVID
crisis. The two best performing methodologies in the analysis were long
short-term memory artificial neural networks (LSTM) and Bayesian vector
autoregression (BVAR). To facilitate further application and testing of each of
the examined methodologies, an open-source repository containing boilerplate
code that can be applied to different datasets is published alongside the
paper, available at: github.com/dhopp1/nowcasting_benchmark.
