Compara mètodes

	Model de Lag Distribuït Autorregressiu No Lineal (NARDL)	Regressió per Mínims Quadrats Ordinàris (MQO)	Model d'Autoregressió Vectorial (VAR)
Camp	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Any d'origen	2014	2019	2005
Autor original	Shin, Yu, and Greenwood-Nimmo	Wooldridge (textbook treatment); classical least squares	Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition
Tipus	Nonlinear cointegration model	Linear regression	Multivariate time-series model
Font seminal	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
Àlies	NARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds test	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon
Relacionats	4	5	4
Resum	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.	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).	Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005).