Neutrosophic Inverse Gaussian Distribution in Economic Policy Design under Indeterminacy

Uncertainty and indeterminacy are central concepts in economic policy design. The inherent uncertainty is difficult to incorporate into traditional statistical models. To overcome the limitation of classical inverse Gaussian (IG) distribution for managing the imprecise data, we examine the structure of neutrosophic inverse Gaussian distribution (NIGD) which is an expansion of the classical IG distribution under neutrosophic framework. The IG distribution, which is commonly employed for reliability analysis and in financial modeling, features a positively skewed curve which makes it is an appropriate index for modeling asymmetric economic data, including risk assessments, investment returns, as well as financial duration. That model includes degrees of truth, lack of information and degrees of falsity, elements that enable policymakers to compute economic variables with incomplete / imprecise information. The mean, variance, skewness and kurtosis of NIGD are derived in neutrosophic environment. The quantile function of the proposed is derived which is further utilized to generate random samples from the proposed model. The utilization of the distribution in economic uncertainty modeling is described using two numerical examples.