ScholarGate
Asistent

Uporedite metode

Pregledajte izabrane metode jednu pored druge; redovi koji se razlikuju su istaknuti.

Nelinearni generalisani metod najmanjih kvadrata (NGLS)×Regresije naizgled nepovezanih jednačina (SUR)×
OblastEkonometrijaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka19751962
TvoracGallant (1975); extended by Davidson & MacKinnonArnold Zellner
TipNonlinear estimatorSystem regression (multi-equation)
Temeljni izvorGallant, A. R. (1987). Nonlinear Statistical Models. Wiley. ISBN: 978-0471802600Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368. DOI ↗
Drugi naziviNGLS, nonlinear generalized least squares, feasible nonlinear GLS, FNGLSSUR, Zellner's SUR, seemingly unrelated regression equations, Görünürde İlişkisiz Regresyon (SUR)
Srodne25
SažetakNonlinear Generalized Least Squares extends the classical GLS framework to regression models where the mean function is nonlinear in the parameters. It accounts for non-spherical errors — heteroscedasticity or autocorrelation — by pre-weighting the nonlinear objective with an estimated error covariance matrix, yielding consistent and asymptotically efficient estimates.Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS.
ScholarGateSkup podataka
  1. v1
  2. 2 Izvori
  3. PUBLISHED
  1. v1
  2. 1 Izvori
  3. PUBLISHED

Idi na pretragu Preuzmi slajdove

ScholarGateUporedite metode: Nonlinear GLS · Seemingly Unrelated Regression. Preuzeto 2026-06-18 sa https://scholargate.app/sr/compare