Comparar métodos

	NARDL Bayesiano: ARDL Não Linear com Estimação Bayesiana	Estimador GMM de Arellano-Bond
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	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
Tipo	Nonlinear cointegrating model with Bayesian inference	GMM estimator for dynamic panel data
Fonte 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.
Outros nomes	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator
Relacionados	6	5
Resumo	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.