Sammenlign metoder

	Nonlineær Autoregressiv Distribueret Lag Model (NARDL)	Granger-kausalitetstest	Almindelig mindste kvadraters metode (OLS) regression
Fagområde	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Oprindelsesår	2014	1969	2019
Ophavsperson	Shin, Yu, and Greenwood-Nimmo	Clive W. J. Granger	Wooldridge (textbook treatment); classical least squares
Type	Nonlinear cointegration model	Time-series predictive causality test	Linear regression
Oprindelig kilde	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281-314). Springer.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Aliasser	NARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds test	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relaterede	4	5	5
Resumé	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods cannot capture.	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.	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).