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Ujian Had Bayesian ARDL×Model ARDL Taklinear (NARDL)×
BidangEkonometrikEkonometrik
KeluargaRegression modelRegression model
Tahun asal2001 (ARDL); Bayesian extension 2010s2014
PengasasPesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literatureShin, Yu & Greenwood-Nimmo
JenisCointegration / bounds testingNonlinear cointegration model
Sumber perintisPesaran, 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. DOI ↗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. link ↗
AliasBayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds testNARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Berkaitan55
RingkasanThe 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.The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.
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ScholarGateBandingkan kaedah: Bayesian ARDL Bounds Test · Nonlinear ARDL. Dicapai 2026-06-18 daripada https://scholargate.app/ms/compare