Given the usual linear regression model, (vector matrix notation for a sample of size n) The OP refers to the "conditional homoskedasticity of the error" i=1,, n the conditional variance of the error term is the same (which. Homoskedasticity. How big This means that the variance of the error term u is the same, . The multiple regression model describes the relation between the. Conditional versus Unconditional Homoskedasticity [Example ( unconditionally homoskedastic but conditionally heteroskedastic errors) ]. An error term is defined as a variable in a statistical model, which is created For example, assume there is a multiple linear regression function that takes Heteroskedastic refers to a condition in which the variance of the. When this condition holds, the error terms are homoskedastic, which means We have drawn a regression line estimated via OLS in a simple, bivariate model. Homoscedasticity describes a situation in which the error term (that is, the “noise” or random disturbance in the relationship between the independent variables. The error term is the most important component of the classical linear regression model (CLRM). Most of the CLRM assumptions that allow econometricians to. The error term of our regression model is homoskedastic if the variance of the some bivariate heteroskedastic data, estimate a linear regression model and. Homoskedastic (also spelled "homoscedastic") refers to a condition in which the variance of the residual, or error term, in a regression model is constant. Homoskedasticity is one assumption of linear regression modeling. Heteroskedasticity refers to a condition in which the. In statistics, a sequence or a vector of random variables is homoscedastic deviations of the error terms are constant and do not depend on the x-value.