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Robust NARDL×Regressão Quantílica×
ÁreaEconometriaEconometria
FamíliaRegression modelRegression model
Ano de origem2014–2020s1978
Autor originalExtension of Shin, Yu & Greenwood-Nimmo (2014) NARDL framework with robust (outlier-resistant) estimationKoenker & Bassett
TipoNonlinear time-series regression with robust estimationConditional quantile regression
Fonte seminalShin, 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 (pp. 281–314). Springer. DOI ↗Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗
Outros nomesRobust Nonlinear ARDL, Outlier-Robust NARDL, Robust Asymmetric ARDL, R-NARDLconditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados35
ResumoRobust NARDL marries the asymmetric cointegration framework of Shin, Yu, and Greenwood-Nimmo (2014) with outlier-resistant estimation. It decomposes a regressor into positive and negative partial sums, tests for asymmetric long-run relationships via a bounds test, and replaces the OLS criterion with an M- or MM-estimator to guard against leverage points and additive outliers common in macroeconomic and financial time series.Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.
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ScholarGateComparar métodos: Robust NARDL · Quantile Regression. Recuperado em 2026-06-15 de https://scholargate.app/pt/compare