Sammenlign metoder

	Wild Bootstrap for Regression Inference	Blokk-bootstrap (Moving Block og Stationary)	Minste kvadraters metode (OLS)
Fagfelt	Statistikk	Statistikk	Økonometri
Familie	Regression model	Regression model	Regression model
Opprinnelsesår	1986	1989	2019
Opphavsperson	Wu (1986); refined by Davidson & Flachaire (2008)	Künsch (moving block, 1989); Politis & Romano (stationary, 1994)	Wooldridge (textbook treatment); classical least squares
Type	Resampling-based regression inference	Resampling inference for dependent data	Linear regression
Opprinnelig kilde	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.	Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap	moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterte	5	5	5
Sammendrag	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.	Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994).	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).