Hualde Bilbao, Javier

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https://academica-e.unavarra.es/handle/2454/49249

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Hualde Bilbao

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Javier

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Economía

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INARBE. Institute for Advanced Research in Business and Economics

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0000-0001-5516-6508

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88719

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8693

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2012 - 20172
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Subject: Fractional integration ×

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    Estimation of the cointegrating rank in fractional cointegration
    (2012) Hualde Bilbao, Javier; Economía; Ekonomia
    This paper proposes an estimator of the cointegrating rank of a potentially cointegrated multivariate fractional process. Our setting is very flexible, allowing the individual observable processes to have different integration orders. The proposed method is automatic and can be also employed to infer the dimensions of possible cointegrating subspaces, which are characterized by special directions in the cointegrating space which generate cointegrating errors with smaller integration orders, increasing the “achievement” of the cointegration analysis. A Monte Carlo experiment of finite sample performance and an empirical analysis are included.
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    PublicationOpen Access
    Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes
    (Elsevier, 2017) Hualde Bilbao, Javier; Iacone, Fabrizio; Economía; Ekonomia
    We consider inference for the mean of a general stationary process based on standardizing the sample mean by a frequency domain estimator of the long run variance. Here, the main novelty is that we consider alternative asymptotics in which the bandwidth is kept fixed. This does not yield a consistent estimator of the long run variance, but, for the weakly dependent case, the studentized sample mean has a Student- limit distribution, which, for any given bandwidth, appears to be more precise than the traditional Gaussian limit. When data are fractionally integrated, the fixed bandwidth limit distribution of the studentized mean is not standard, and we derive critical values for various bandwidths. By a Monte Carlo experiment of finite sample performance we find that this asymptotic result provides a better approximation than other proposals like the test statistic based on the Memory Autocorrelation Consistent (MAC) estimator of the variance of the sample mean.