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Smooth Transition Autoregressive (STAR) Modell×System GMM (Arellano-Bover / Blundell-Bond)×
FagfeltØkonometriØkonometri
FamilieRegression modelRegression model
Opprinnelsesår19941998
OpphavspersonTeräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)Arellano & Bover (1995); Blundell & Bond (1998)
TypeNonlinear time-series regime-switching modelDynamic panel data estimator
Opprinnelig kildeTeräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗Arellano, M. & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies, 58(2), 277-297. DOI ↗
Aliassmooth transition autoregressive model, LSTAR, ESTAR, logistic STARArellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
Relaterte44
SammendragThe Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.System GMM is a generalized method of moments estimator for dynamic panel models that contain a lagged dependent variable. Introduced by Blundell and Bond (1998), building on Arellano and Bover, it augments the differenced equation of the earlier difference GMM (Arellano-Bond) with the equation in levels to deliver consistent estimates when N is large and T is small.
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ScholarGateSammenlign metoder: STAR Model · System GMM. Hentet 2026-06-18 fra https://scholargate.app/no/compare