Võrdle meetodeid

	Bayesi NARDL: mittelineaarne ARDL Bayesi hindamisega	Arellano-Bongi GMM-hinnang
Valdkond	Ökonomeetria	Ökonomeetria
Perekond	Regression model	Regression model
Tekkeaasta	2014 (NARDL); Bayesian extension c. 2015–2020	1991
Looja	Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature	Manuel Arellano and Stephen Bond
Tüüp	Nonlinear cointegrating model with Bayesian inference	GMM estimator for dynamic panel data
Algallikas	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.	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.
Rööpnimetused	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator
Seotud	6	5
Kokkuvõte	Bayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior knowledge and producing full posterior distributions over all parameters, including the asymmetry gap.	The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous.