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  2. Heteroskedasticity-consistent standard errors - Wikipedia

    en.wikipedia.org/wiki/Heteroskedasticity...

    Heteroskedasticity-consistent standard errors that differ from classical standard errors may indicate model misspecification. Substituting heteroskedasticity-consistent standard errors does not resolve this misspecification, which may lead to bias in the coefficients. In most situations, the problem should be found and fixed. [5]

  3. White test - Wikipedia

    en.wikipedia.org/wiki/White_test

    White test is a statistical test that establishes whether the variance of the errors in a regression model is constant: that is for homoskedasticity. This test, and an estimator for heteroscedasticity-consistent standard errors , were proposed by Halbert White in 1980. [ 1 ]

  4. Homoscedasticity and heteroscedasticity - Wikipedia

    en.wikipedia.org/wiki/Homoscedasticity_and...

    Heteroscedasticity-consistent standard errors (HCSE), while still biased, improve upon OLS estimates. [2] HCSE is a consistent estimator of standard errors in regression models with heteroscedasticity. This method corrects for heteroscedasticity without altering the values of the coefficients.

  5. Newey–West estimator - Wikipedia

    en.wikipedia.org/wiki/Newey–West_estimator

    A Newey–West estimator is used in statistics and econometrics to provide an estimate of the covariance matrix of the parameters of a regression-type model where the standard assumptions of regression analysis do not apply. [1]

  6. Breusch–Pagan test - Wikipedia

    en.wikipedia.org/wiki/Breusch–Pagan_test

    In statistics, the Breusch–Pagan test, developed in 1979 by Trevor Breusch and Adrian Pagan, [1] is used to test for heteroskedasticity in a linear regression model. It was independently suggested with some extension by R. Dennis Cook and Sanford Weisberg in 1983 (Cook–Weisberg test). [2]

  7. Weighted least squares - Wikipedia

    en.wikipedia.org/wiki/Weighted_least_squares

    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.

  8. Goldfeld–Quandt test - Wikipedia

    en.wikipedia.org/wiki/Goldfeld–Quandt_test

    The parametric Goldfeld–Quandt test offers a simple and intuitive diagnostic for heteroskedastic errors in a univariate or multivariate regression model. However some disadvantages arise under certain specifications or in comparison to other diagnostics, namely the Breusch–Pagan test , as the Goldfeld–Quandt test is somewhat of an ad hoc ...

  9. Autoregressive conditional heteroskedasticity - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_conditional...

    In 2004, Claudia Klüppelberg, Alexander Lindner and Ross Maller proposed a continuous-time generalization of the discrete-time GARCH(1,1) process.The idea is to start with the GARCH(1,1) model equations