Benchmarking Econometric and Machine Learning Methodologies in Nowcasting

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: UNCTAD research paper

سال: 2022

ISSN: ['2708-2814']

DOI: https://doi.org/10.18356/27082814-83