000121064 001__ 121064 000121064 005__ 20240319080946.0 000121064 0247_ $$2doi$$a10.1080/07350015.2020.1773834 000121064 0248_ $$2sideral$$a118842 000121064 037__ $$aART-2022-118842 000121064 041__ $$aeng 000121064 100__ $$aCamacho, Máximo 000121064 245__ $$aA new approach to dating the reference cycle 000121064 260__ $$c2022 000121064 5060_ $$aAccess copy available to the general public$$fUnrestricted 000121064 5203_ $$aThis article proposes a new approach to the analysis of the reference cycle turning points, defined on the basis of the specific turning points of a broad set of coincident economic indicators. Each individual pair of specific peaks and troughs from these indicators is viewed as a realization of a mixture of an unspecified number of separate bivariate Gaussian distributions whose different means are the reference turning points. These dates break the sample into separate reference cycle phases, whose shifts are modeled by a hidden Markov chain. The transition probability matrix is constrained so that the specification is equivalent to a multiple change-point model. Bayesian estimation of finite Markov mixture modeling techniques is suggested to estimate the model. Several Monte Carlo experiments are used to show the accuracy of the model to date reference cycles that suffer from short phases, uncertain turning points, small samples, and asymmetric cycles. In the empirical section, we show the high performance of our approach to identifying the US reference cycle, with little difference from the timing of the turning point dates established by the NBER. In a pseudo real-time analysis, we also show the good performance of this methodology in terms of accuracy and speed of detection of turning point dates. 000121064 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/ECO2017-83255-C3-1-P$$9info:eu-repo/grantAgreement/ES/MICINN/ECO2017-83255-C3-3-P 000121064 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttp://creativecommons.org/licenses/by-nc/3.0/es/ 000121064 590__ $$a3.0$$b2022 000121064 592__ $$a6.15$$b2022 000121064 591__ $$aSTATISTICS & PROBABILITY$$b17 / 125 = 0.136$$c2022$$dQ1$$eT1 000121064 593__ $$aEconomics and Econometrics$$c2022$$dQ1 000121064 591__ $$aSOCIAL SCIENCES, MATHEMATICAL METHODS$$b16 / 53 = 0.302$$c2022$$dQ2$$eT1 000121064 593__ $$aStatistics, Probability and Uncertainty$$c2022$$dQ1 000121064 591__ $$aECONOMICS$$b130 / 380 = 0.342$$c2022$$dQ2$$eT2 000121064 593__ $$aStatistics and Probability$$c2022$$dQ1 000121064 593__ $$aSocial Sciences (miscellaneous)$$c2022$$dQ1 000121064 594__ $$a7.6$$b2022 000121064 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000121064 700__ $$0(orcid)0000-0001-6609-4247$$aGadea, María Dolores$$uUniversidad de Zaragoza 000121064 700__ $$aGómez Loscos, Ana 000121064 7102_ $$14014$$2225$$aUniversidad de Zaragoza$$bDpto. Economía Aplicada$$cÁrea Economía Aplicada 000121064 773__ $$g40, 1 (2022), 66-81$$pJ. bus. econ. stat.$$tJOURNAL OF BUSINESS & ECONOMIC STATISTICS$$x0735-0015 000121064 8564_ $$s1882253$$uhttps://zaguan.unizar.es/record/121064/files/texto_completo.pdf$$yPostprint 000121064 8564_ $$s1673480$$uhttps://zaguan.unizar.es/record/121064/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000121064 909CO $$ooai:zaguan.unizar.es:121064$$particulos$$pdriver 000121064 951__ $$a2024-03-18-12:33:16 000121064 980__ $$aARTICLE