Usporedite metode

	Robustni model nelinearne autoregresivne distribuirane stope (Robust NARDL)	Regresija običnih najmanjih kvadrata (OLS)
Područje	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model
Godina nastanka	2014–2020s	2019
Tvorac	Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL framework with robust (outlier-resistant) estimation	Wooldridge (textbook treatment); classical least squares
Vrsta	Nonlinear time-series regression with robust estimation	Linear regression
Temeljni izvor	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
Drugi nazivi	Robust Nonlinear ARDL, Outlier-Robust NARDL, Robust Asymmetric ARDL, R-NARDL	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Srodne	3	5
Sažetak	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).