手法を比較

	ロバスト非線形自己回帰分布ラグ (Robust NARDL) モデル	最小二乗法 (OLS) 回帰
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2014–2020s	2019
提唱者	Extension of Shin, Yu & Greenwood-Nimmo (2014) NARDL framework with robust (outlier-resistant) estimation	Wooldridge (textbook treatment); classical least squares
種類	Nonlinear time-series regression with robust estimation	Linear regression
原典	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
別名	Robust Nonlinear ARDL, Outlier-Robust NARDL, Robust Asymmetric ARDL, R-NARDL	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
関連	3	5
概要	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).