Compară metode

	Bootstrap sălbatic pentru inferență în regresie	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Statistică	Econometrie
Familie	Regression model	Regression model
Anul apariției	1986	2019
Autorul original	Wu (1986); refined by Davidson & Flachaire (2008)	Wooldridge (textbook treatment); classical least squares
Tip	Resampling-based regression inference	Linear regression
Sursa seminală	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Denumiri alternative	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Înrudite	5	5
Rezumat	The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.	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).