Сравнение на методи

	Стандартни грешки на Нюи-Уест за HAC	Метод на най-малките квадрати (МНК)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1987	2019
Създател	Whitney Newey & Kenneth West	Wooldridge (textbook treatment); classical least squares
Тип	Covariance matrix estimator	Linear regression
Основополагащ източник	Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Други названия	HAC standard errors, Heteroskedasticity and Autocorrelation Consistent covariance, Bartlett kernel HAC estimator, HAC düzeltmeli standart hatalar	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Свързани	1	5
Резюме	Newey-West HAC standard errors, introduced by Whitney Newey and Kenneth West in 1987, provide a covariance matrix estimator for OLS regression that remains valid under both heteroskedasticity and serial autocorrelation of unknown form. They are the standard tool for correcting inference in time-series and panel regression when residuals are not i.i.d., requiring no specification of the error structure beyond choosing a bandwidth parameter.	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).