Hualde Bilbao, Javier

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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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Now showing 1 - 4 of 4
  • PublicationOpen Access
    Fixed bandwidth inference for fractional cointegration
    (Wiley, 2019) Hualde Bilbao, Javier; Iacone, Fabrizio; Economía; Ekonomia
    In a fractional cointegration setting we derive the fixed bandwidth limiting theory of a class of estimators of the cointegrating parameter which are constructed as ratios of weighted periodogram averages. These estimators offer improved limiting properties over those of more standard approaches like ordinary least squares or narrow band least squares estimation. These advantages have been justified by means of traditional asymptotic theory and here we explore whether these improvements still hold when considering the alternative fixed bandwidth theory and, more importantly, whether this latter approach provides a more accurate approximation to the sampling distribution of the corresponding test statistics. This appears to be relevant, especially in view of the typical oversizing displayed by Wald statistics when confronted to the standard limiting theory. A Monte Carlo study of finite-sample behaviour is included.
  • 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.
  • PublicationOpen Access
    Small‐b and fixed‐b asymptotics for weighted covariance estimation in fractional cointegration
    (Wiley, 2015) Hualde Bilbao, Javier; Iacone, Fabrizio; Economía; Ekonomia
    In a standard cointegrating framework, Phillips (1991) introduced the weighted covariance (WC) estimator of cointegrating parameters. Later, Marinucci (2000) applied this estimator to fractional circumstances and, like Phillips (1991), analysed the so-called small-b asymptotic approximation to its sampling distribution. Recently, an alternative limiting theory (fixed-b asymptotics) has been successfully employed to approximate sampling distributions. With the purpose of comparing both approaches, we derive here the fixed-b limit of WC estimators in a fractional setting, filling also some gaps in the traditional (small-b) theory. We also provide some Monte Carlo evidence that suggests that the fixed-b limit is more accurate.
  • PublicationOpen Access
    Revisiting inflation in the euro area allowing for long memory
    (Elsevier, 2017) Hualde Bilbao, Javier; Iacone, Fabrizio; Economía; Ekonomia
    We analyse inflation and inflation differentials in the euro area allowing for long memory and a new type of limiting theory denoted fixed-bandwidth. Our results differ from those based on standard normal asymptotics and the short memory assumption, and we also find that the inflation differentials between 'core' and 'peripheral' countries are strongly persistent. 'Core' economies appear to have less persistent differentials and may be more integrated, while 'peripheral' countries with high inflation may find themselves under competitive pressure for a long time.