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Modelo de Defasagem Autorregressiva Não Linear Distribuída (NARDL)×Regressão por Mínimos Quadrados Ordinários (MQO)×
ÁreaEconometriaEconometria
FamíliaRegression modelRegression model
Ano de origem20142019
Autor originalShin, Yu, and Greenwood-NimmoWooldridge (textbook treatment); classical least squares
TipoNonlinear cointegration modelLinear regression
Fonte seminalShin, 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. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Outros nomesNARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds testordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados45
ResumoThe 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).
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ScholarGateComparar métodos: Nonlinear NARDL · OLS Regression. Recuperado em 2026-06-15 de https://scholargate.app/pt/compare