Comparer des méthodes

	Modèle Bayésien ARIMA	Test des bornes bayésien ARDL
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1970s (ARIMA); Bayesian extension prominent from 1990s	2001 (ARDL); Bayesian extension 2010s
Auteur d'origine	Pole, West & Harrison (Bayesian treatment); Box & Jenkins (ARIMA foundation)	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature
Type	Bayesian time series model	Cointegration / bounds testing
Source fondatrice	Pole, A., West, M., & Harrison, J. (1994). Applied Bayesian Forecasting and Time Series Analysis. Chapman & Hall. ISBN: 978-0412416903	Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.
Alias	Bayesian ARIMA, BARIMA, Bayesian Box-Jenkins model, Bayesian integrated time series model	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test
Apparentées	6	5
Résumé	The Bayesian ARIMA model combines the classical Box-Jenkins ARIMA framework with Bayesian inference. Instead of obtaining single point estimates for autoregressive and moving average parameters, it places prior distributions over them and uses observed data to update beliefs into a full posterior distribution, enabling coherent uncertainty quantification and probabilistic forecasting.	The Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one.