Porovnat metody

	Model NARDL s časově proměnnými parametry (TVP-NARDL)	Model nelineární autoregresní distribuované zpoždění (NARDL)	Regrese s prahovou hodnotou
Obor	Ekonometrie	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model	Regression model
Rok vzniku	2019 (TVP extension); 2014 (NARDL base)	2014	2000
Tvůrce	Bagnai & Ospina-Rojas (TVP extension); NARDL base by Shin, Yu & Greenwood-Nimmo	Shin, Yu & Greenwood-Nimmo	Bruce E. Hansen
Typ	Nonlinear time-series model with time-varying coefficients	Asymmetric cointegration / error-correction model	Nonlinear regime-switching regression
Původní zdroj	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. Horrace & R. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.	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.	Hansen, B. E. (2000). Sample Splitting and Threshold Estimation. Econometrica, 68(3), 575-603.
Další názvy	TVP-NARDL, time-varying NARDL, rolling NARDL, dynamic asymmetric ARDL	nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL)	threshold model, regime-switching regression, sample splitting model, Eşik Değer Regresyonu (Threshold Regression)
Příbuzné	3	4	5
Shrnutí	The Time-Varying Parameter NARDL (TVP-NARDL) model extends the Nonlinear ARDL framework by allowing the coefficients on positive and negative partial sums of a regressor to change over time. This combination captures both asymmetric responses and structural instability in long-run and short-run relationships within a single cointegrating specification.	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.	Threshold regression is a nonlinear, regime-switching model in which the regression parameters take different values above and below an estimated threshold value of a threshold variable. The sample-splitting and threshold-estimation framework was developed by Bruce E. Hansen (2000) and is widely used for time-series and panel data with structural breaks and regime-dependent relationships.