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How does heteroskedasticity affect variance?
Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.
When the errors are Heteroskedastic then?
When this condition holds, the error terms are homoskedastic, which means the errors have the same scatter regardless of the value of X. When the scatter of the errors is different, varying depending on the value of one or more of the independent variables, the error terms are heteroskedastic.
Which is the variance of the unknown errors?
That could very well be the case. β ^ would be a parameter vector rather than a single parameter. Its variance would be a matrix rather than a single number. In a linear regression model of the form y = β 0 + β 1 x 1 + ⋯ + β K x K + ε, variance of the unknown errors would be the variance of ε, denoted σ 2; it is a single number (not a matrix).
What do you mean by heteroskedasticity in science?
• Heteroskedasticity means that the variance of the errors is not constant across observations. • In particular the variance of the errors may be a function of explanatory variables. • Think of food expenditure for example. It may well be that the “diversity of taste” for food is greater for wealthier people than for poor people.
Standard errors based on this procedure are called (heteroskedasticity) robust standard errors or White-Huber standard errors. Or it is also known as the sandwich estimator of variance (because of how the calculation formula looks like). This procedure is reliable but entirely empirical. We do not impose any assumptions on the
When does the variance of A μI remain the same?
The variance of each μi remains the same irrespective of small or large values of the explanatory variable i.e. σ μ 2 is not function of Xi i.e σ μ i 2 ≠ f ( X i). If the assumption of homoscedastic disturbance (Constant Variance) is not fulfilled, following are the consequence