Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace:

Minimum distance estimation of stationary and non-stationary ARFIMA processes
Mayoral, Laura
Universitat Pompeu Fabra. Departament d'Economia i Empresa
A new parametric minimum distance time-domain estimator for ARFIMA processes is introduced in this paper. The proposed estimator minimizes the sum of squared correlations of residuals obtained after filtering a series through ARFIMA parameters. The estimator iseasy to compute and is consistent and asymptotically normally distributed for fractionallyintegrated (FI) processes with an integration order d strictly greater than -0.75. Therefore, it can be applied to both stationary and non-stationary processes. Deterministic components are also allowed in the DGP. Furthermore, as a by-product, the estimation procedure provides an immediate check on the adequacy of the specified model. This is so because the criterion function, when evaluated at the estimated values, coincides with the Box-Pierce goodness of fit statistic. Empirical applications and Monte-Carlo simulations supporting the analytical results and showing the good performance of the estimator in finite samples are also provided.
Operations Management
fractional integration
nonstationary long-memory time series
minimum distance estimation
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
Documento de trabajo

Mostrar el registro completo del ítem