Confronta i metodi

	Il test ai limiti ARDL (ARDL Bounds Test)	Modello Autoregressivo a Ritardi Distribuiti Non Lineare (NARDL)	Regressione a soglia
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	2001	2014	2000
Ideatore	Pesaran, Shin & Smith	Shin, Yu & Greenwood-Nimmo	Bruce E. Hansen
Tipo	Cointegration test / Autoregressive distributed lag model	Asymmetric cointegration / error-correction model	Nonlinear regime-switching regression
Fonte seminale	Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326.	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.
Alias	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)	nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL)	threshold model, regime-switching regression, sample splitting model, Eşik Değer Regresyonu (Threshold Regression)
Correlati	4	4	5
Sintesi	The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations.	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.