Compara mètodes

	Bayesian NARDL: Autoregressió Distribuïda No Lineal amb Estimació Bayesiana	Estimador GMM d'Arellano-Bond
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2014 (NARDL); Bayesian extension c. 2015–2020	1991
Autor original	Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature	Manuel Arellano and Stephen Bond
Tipus	Nonlinear cointegrating model with Bayesian inference	GMM estimator for dynamic panel data
Font seminal	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.
Àlies	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator
Relacionats	6	5
Resum	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.