Tax fraud represents a significant economic challenge worldwide. The IRS $($Internal Revenue Service$)$ is tasked with projecting U$.$S$.$ tax revenues over the next decade using historical data from a database that includes citizens' income, age, and race. To achieve a more accurate forecast, the IRS is considering several regression models to analyze the relationship represented by the equation:

PaidTAXAmount $=$ f$($income$,$ age, race$)+$ e

where $"$i$"$ denotes an individual in the income database. Given the complexity of the data, particularly the similarity among high$-$income individuals which could violate the Independence of Irrelevant Alternatives $($IIA$)$ property, the IRS needs to select the most appropriate model for prediction.

Which of these models would be most appropriate for accurately predicting future U$.$S$.$ tax income?

A$)$Multinomial Probit, due to the potential violation of the IIA property among high$-$income individuals.

B$)$Tobit Regression, to account for censoring effects in the data.

C$)$Truncated Linear Regression, for data that excludes observations beyond certain income thresholds.

D$)$Truncated Linear Regression with a quadratic term $($income squared$),$ to address non$-$linear trends in tax amounts paid by