https://hal-centralesupelec.archives-ouvertes.fr/hal-01578357Souza, IgorIgorSouzaUFMG - Universidade Federal de Minas Gerais [Belo Horizonte]Reisen, ValderioValderioReisenUniversade Federal Do Espirito Santo - Universade Federal Do Espirito SantoFranco, GlauraGlauraFrancoUFMG - Universidade Federal de Minas Gerais [Belo Horizonte]Bondon, PascalPascalBondonL2S - Laboratoire des signaux et systèmes - UP11 - Université Paris-Sud - Paris 11 - CentraleSupélec - CNRS - Centre National de la Recherche ScientifiqueThe Estimation and Testing of the Cointegration Order Based on the Frequency DomainHAL CCSD2018[STAT.TH] Statistics [stat]/Statistics Theory [stat.TH]Bondon, Pascal2017-08-29 09:59:132023-03-09 10:30:162017-08-29 09:59:13enJournal articles10.1080/07350015.2016.12514421This article proposes a method to estimate the degree of cointegration in bivariate series and suggests a test statistic for testing noncointegration based on the determinant of the spectral density matrix for the frequencies close to zero. In the study, series are assumed to be I(d), 0 < d ⩽ 1, with parameter d supposed to be known. In this context, the order of integration of the error series is I(d − b), b ∈ [0, d]. Besides, the determinant of the spectral density matrix for the dth difference series is a power function of b. The proposed estimator for b is obtained here performing a regression of logged determinant on a set of logged Fourier frequencies. Under the null hypothesis of noncointegration, the expressions for the bias and variance of the estimator were derived and its consistency property was also obtained. The asymptotic normality of the estimator, under Gaussian and non-Gaussian innovations, was also established. A Monte Carlo study was performed and showed that the suggested test possesses correct size and good power for moderate sample sizes, when compared with other proposals in the literature. An advantage of the method proposed here, over the standard methods, is that it allows to know the order of integration of the error series without estimating a regression equation. An application was conducted to exemplify the method in a real context.