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Robusni generalisani metod najmanjih kvadrata (Robusni GLS)

Robusni GLS proširuje klasični generalisani metod najmanjih kvadrata (GLS) uparivanjem procene koeficijenata GLS-a sa standardnim greškama konzistentnim sa heteroskedasticnošću i autokorelacijom (HAC), ili korišćenjem M-procene u okviru GLS okvira. On korigira nesferne greške — heteroskedasticnost, autokorelaciju, ili oboje — istovremeno štiteći inferencu od pogrešne specifikacije kovarijantne strukture grešaka.

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Izvori

  1. Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
  2. White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838. DOI: 10.2307/1912934

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Robust Generalized Least Squares. ScholarGate. https://scholargate.app/sr/econometrics/robust-gls

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Citirana u

ScholarGateRobust GLS (Robust Generalized Least Squares). Preuzeto 2026-06-15 sa https://scholargate.app/sr/econometrics/robust-gls · Skup podataka: https://doi.org/10.5281/zenodo.20539026