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Consider the linear regression equation = +, =, …,, where the dependent random variable equals the deterministic variable times coefficient plus a random disturbance term that has mean zero. The disturbances are homoscedastic if the variance of ε i {\displaystyle \varepsilon _{i}} is a constant σ 2 {\displaystyle \sigma ^{2}} ; otherwise ...
In a sample of T residuals under the null hypothesis of no ARCH errors, the test statistic T'R² follows distribution with q degrees of freedom, where ′ is the number of equations in the model which fits the residuals vs the lags (i.e. ′ =).
Generalized estimating equations; Weighted least squares, an alternative formulation; White test — a test for whether heteroskedasticity is present. Newey–West estimator; Quasi-maximum likelihood estimate
[1] [2] [3] Assuming a variable is homoscedastic when in reality it is heteroscedastic (/ ˌ h ɛ t ər oʊ s k ə ˈ d æ s t ɪ k /) results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient.
[2] [3] Stephen Goldfeld and Richard E. Quandt raise concerns about the assumed structure, cautioning that the v i may be heteroscedastic and otherwise violate assumptions of ordinary least squares regression. [4]
In Julia, the CovarianceMatrices.jl package [11] supports several types of heteroskedasticity and autocorrelation consistent covariance matrix estimation including Newey–West, White, and Arellano.
Weighted least squares (WLS), also known as weighted linear regression, [1] [2] is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance of observations (heteroscedasticity) is incorporated into the regression.
Heteroscedastic Model [1]: Each set of random variables in ... That is, the vector represents the solution to the following system of equations ...