Local Whittle estimation of long memory: Standard versus bias-reducing techniques

Javier García-Enríquez and Javier Hualde
Authors registered in the RePEc Author Service: Javier García Enríquez

Econometrics and Statistics, 2019, vol. 12, issue C, 66-77

Abstract: Frequency domain semiparametric estimation of memory parameters belongs to the standard toolkit of applied time series researchers. These methods are based on a local approximation of the spectral density, which robustifies the estimation methods against misspecification, but induces a loss with respect to the parametric setting, where the spectral density is known up to a finite number of unknown parameters. In particular, standard semiparametric estimators have convergence rates no better than T2/5, whereas the rate T1/2 is achievable under parametric assumptions. Refinements of the local approximation have been developed by means of bias-reducing techniques, implying that rates arbitrarily close to the parametric one are achievable in the semiparametric setting. Two of these approaches to cover more general settings (including non-stationarity) are extended. A Monte Carlo experiment of finite sample performance is used to assess whether the asymptotic advantages of the bias-reducing methods materialize in better finite sample behavior.

Keywords: Memory parameters; Semiparametric estimation; Standard versus bias-reducing techniques; Fractionally integrated processes (search for similar items in EconPapers)
JEL-codes: C22 C32 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:12:y:2019:i:c:p:66-77

DOI: 10.1016/j.ecosta.2019.05.004

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