Fiscal communication and its effects on monetary policy: an analysis based on natural language processing
Keywords:
Machine Learning., Textual analysis, Natural Language Processing, Fiscal DominanceAbstract
The purpose of this article is to investigate how the tone of the fiscal authority's communications can influence monetary policy decisions. To this end, a fiscal policy sentiment index was developed using machine learning techniques, using the monthly public debt reports issued by the National Treasury as a source of information. The sentiment index was used as an explanatory variable in two approaches to achieve the central objective of the article. In the first, a traditional version of the central bank's reaction function was estimated using classic econometric techniques. In the second, the analysis was expanded towards a Dynamic Stochastic General Equilibrium model (DSGE), in order to estimate central bank reaction functions and, with this, produce inferences about the effect of fiscal policy sentiment in the behavior of monetary policy. The main results suggest that fiscal policy sentiment has influenced the monetary policy decision-making process in Brazil, indicating a possible scenario of fiscal dominance. In this sense, this article contributes an unprecedented approach to an important topic in public finance by reinforcing the fundamental role of communication and coordination between monetary and fiscal authorities.
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