Сравнение на методи

	Тест на Уайт за хетероскедастичност	Метод на най-малките квадрати (МНК)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1980	2019
Създател	Halbert White	Wooldridge (textbook treatment); classical least squares
Тип	General test for heteroskedasticity	Linear regression
Основополагащ източник	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Други названия	White's general heteroskedasticity test, White değişen varyans testi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Свързани	3	5
Резюме	The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-products, so it can detect heteroskedasticity related to any of these terms. The same 1980 paper introduced the heteroskedasticity-consistent ('White') standard errors that are the standard remedy when the test rejects.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).