Comparer des méthodes

	Robust NARDL	Régression par Moindres Carrés Ordinaires (MCO)	Régression quantile
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	2014–2020s	2019	1978
Auteur d'origine	Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL framework with robust (outlier-resistant) estimation	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Type	Nonlinear time-series regression with robust estimation	Linear regression	Conditional quantile regression
Source fondatrice	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 (pp. 281–314). Springer.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	Robust Nonlinear ARDL, Outlier-Robust NARDL, Robust Asymmetric ARDL, R-NARDL	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	3	5	5
Résumé	Robust 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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.