Compara mètodes

	Model d'Autoregressió Distribuïda No Lineal (NARDL)	Regressió per Mínims Quadrats Ordinàris (MQO)	Regressió quantílica	Model de Transició Suau Autorregressiu (STAR)
Camp	Econometria	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model	Regression model
Any d'origen	2014	2019	1978	1994
Autor original	Shin, Yu & Greenwood-Nimmo	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett	Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002)
Tipus	Asymmetric cointegration / error-correction model	Linear regression	Conditional quantile regression	Nonlinear time-series regime-switching model
Font seminal	Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. 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.	Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218.
Àlies	nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon	smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR
Relacionats	4	5	5	4
Resum	The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently.	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.	The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations.