Time-Varying Dictionary and the Predictive Power of FED Minutes

Luiz Renato Lima (), Lucas Lúcio Godeiro and Mohammed Mohsin
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Luiz Renato Lima: The University of Tennessee
Lucas Lúcio Godeiro: Federal University of the Semi-Arid Region (UFERSA)
Mohammed Mohsin: The University of Tennessee

Computational Economics, 2021, vol. 57, issue 1, No 7, 149-181

Abstract: Abstract This paper proposes a method to extract the most predictive information from FED minutes that is specifically adapted to the problem of forecasting. Instead of considering a dictionary (set of words) with a fixed content, we construct a dictionary whose content is allowed to change over time. Specifically, we utilize machine learning to identify the most predictive words (the most predictive content) of a given minute and use them to derive new predictors. We show that the new predictors improve real time forecasts of output growth by a statistically significant margin, suggesting that the combination of supervised machine learning and text regression can be interpreted as a powerful device for out-of-sample macroeconomic forecasting.

Keywords: Text regression; Supervised machine learning; Elastic net; Central bank communication; Forecasting; real time (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 E47 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-020-10039-9

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