Linganisha mbinu

	Modeli wa Kawaida wa Autoregressive Distributed Lag (Panel NARDL)	Kipimo cha Vikomo vya ARDL (Kipimo cha Vikomo cha Pesaran)
Nyanja	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	2014–2018	2001
Mwanzilishi	Shin, Yu & Greenwood-Nimmo (2014), extended to panel settings by subsequent authors	Pesaran, Shin & Smith
Aina	Nonlinear dynamic panel model	Cointegration test / Autoregressive distributed lag model
Chanzo asilia	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281–314). Springer.	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.
Majina mbadala	Panel Nonlinear ARDL, panel asymmetric ARDL, panel NARDL bounds test, nonlinear panel cointegration model	Pesaran bounds test, bounds testing approach, ARDL cointegration test, ARDL Sınır Testi (Pesaran Bounds Test)
Zinazohusiana	4	4
Muhtasari	Panel NARDL extends the time-series NARDL framework of Shin, Yu and Greenwood-Nimmo (2014) to a panel data setting, allowing researchers to detect asymmetric long-run and short-run relationships between variables across multiple cross-sections simultaneously. By decomposing the regressor into positive and negative partial sums, the model tests whether increases and decreases in an explanatory variable have different effects on the outcome.	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.