Heteroskedastisite

Heteroskedastisite is not a standard term in statistics. In most contexts it appears to be a misspelling or informal variant of heteroskedasticity, the property that the variance of the error term in a regression model is not constant across observations. Because there is no formally adopted definition, the precise meaning of heteroskedastisite can vary; some discussions treat it as a locality- or site-specific form of heteroskedasticity observed in panel or spatial data, while others simply regard it as a misspelling.

In standard terms, heteroskedasticity describes non-constant error variance. It can arise from scaling effects, genuine differences in variability across groups, model misspecification, or measurement error. The main consequence is that conventional standard errors and test statistics may be biased, leading to unreliable inference if not addressed.

Detection relies on both graphical and formal methods. Residual plots (residuals versus fitted values) can reveal patterns of increasing or decreasing variance. Formal tests include the Breusch-Pagan test, White test, and the Goldfeld-Quandt test, among others. Modern econometric software often provides robust options that do not assume constant variance.

Remedies fall into several approaches: using heteroskedasticity-robust standard errors (Huber-White), transforming the dependent variable (for example, a log or Box-Cox transformation), or changing the model to a form that accommodates unequal variances (such as weighted least squares or generalized least squares).

See also: heteroskedasticity, robust standard errors, regression analysis, panel data, spatial econometrics.