Local Whittle estimation of long memory: Standard versus bias-reducing techniques
[EN] 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 wit...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/64358 |
| Acceso en línea: | http://hdl.handle.net/10810/64358 |
| Access Level: | acceso abierto |
| Palabra clave: | memory parameters semiparametric estimation bias-reducing techniques fractionally integrated processes |
| Sumario: | [EN] 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 T^2/5 , whereas the rate T^1/2 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. |
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