Methoden vergleichen

	Nichtlineares Autoregressives Distributives Lag-Modell (NARDL)	Autoregressive Modell mit glatter Übergangsfunktion (STAR-Modell)	System-GMM (Arellano-Bover / Blundell-Bond)
Fachgebiet	Ökonometrie	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model	Regression model
Entstehungsjahr	2014	1994	1998
Urheber	Shin, Yu & Greenwood-Nimmo	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)	Arellano & Bover (1995); Blundell & Bond (1998)
Typ	Asymmetric cointegration / error-correction model	Nonlinear time-series regime-switching model	Dynamic panel data estimator
Wegweisende Quelle	Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer.	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.	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.
Aliasnamen	nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL)	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR	Arellano-Bover estimator, Blundell-Bond estimator, dynamic panel GMM, Sistem GMM (Arellano-Bover / Blundell-Bond)
Verwandt	4	4	4
Zusammenfassung	The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently.	The 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.