Updated: Aug 20, 2021

Having different variances. A set, or a vector, of observations, is heteroscedastic if the variance of the random error is different for different observations. Heteroscedasticity observed in cross-sectional data is typically related to the scale effect: often, larger cross-sectional units are subject to larger values of the random component. In time-series data it may take the form of serial correlation in the variance (autoregressive conditional heteroscedasticity). It may also be introduced by model misspecification. In the presence of heteroscedasticity, the ordinary least squares estimators of the coefficients are consistent but inefficient; those of the standard errors are inconsistent, and hence the standard inference based on the estimated standard errors is invalid. Among the popular tests for heteroscedasticity are the Breusch-Pagan test, the Glejser test, and White’s test. Two approaches to estimation with heteroscedastic data are generalized least squares (both the coefficients and the standard errors are re-estimated) and heteroscedasticity-consistent standard errors (only the estimated standard errors are corrected).

Reference: Oxford Press Dictonary of Economics, 5th edt.

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James Knight
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James is the Editor of Education for Invezz, where he covers topics from across the financial world, from the stock market, to cryptocurrency, to macroeconomic markets.... read more.