Economic Crisis Treatment Based on Artificial Intelligence
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Abstract
Abstract: There are many possible causes of an economic crisis—a financial downturn,
a banking meltdown, political strife (e.g., the Russia-Ukraine war), or a health-related
catastrophe (e.g., Covid-19). Some of these crises are expected, while others are “bolts
from the sky.” However, what is certain is that all these crises, whatever their cause,
have a negative impact on global gross domestic product (GDP). If we can identify the
components of output that have the most impact in an economic crisis, we might be able
to mitigate its effects. Therefore, this paper uses machine learning algorithms to determine
how the components of expenditure and sectoral value-added approach impact global
GDP. The gradient boosting algorithm is the most accurate model for predicting and
determining the impact of independent variables on a dependent variable. The results
indicate that government spending has the largest effect on global GDP, accounting for
68.3% of the impact. The economic sector with the most impact on global GDP is the
service sector, which affects global output by 42.3%, followed by the agricultural sector
at 30.2%. Thus, stimulating government spending and the service sector may reduce the
negative effects of an economic crisis.
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