Nexus of Cryptocurrency and Output gap in Nigeria: A Decision Tree Regression in Machine Learning using Python Programming Language.

Abstract

This study examines how the output gap would react to daily bitcoin series, this is achieved through the use of decision tree regression in machine learning which is estimated in a Python programming language. The gap in literature is in three strands which are basically empirical literature, variable, and methodology. High-powered (daily) data were used in the analysis covering the scope of 2010 to 2022, where Bitcoin was used to proxy cryptocurrency. The Garch model was used to control the volatility in the bitcoin series before carrying out the decision tree regression analysis. The work of Jia et al (2023) was used to lay the assumption of Bitcoin and the prediction of output gap in Nigeria. The result of our analysis shows that bitcoin is predicted to reduce the output gap in Nigeria, which was linked to the fact that macro variables such as inflation can be controlled based on the result. Policy implications from the result were used to conclude the paper.