Semiparametric inference in correlated long memory signal plus noise models
This paper proposes an extension of the log periodogram regression in perturbed long memory series that accounts for the added noise, also allowing for correlation between signal and noise, which represents a common situation in many economic and financial series. Consistency (for d < 1) and asymptotic normality (for d < 3/4) are shown with the same bandwidth restriction as required for the original log periodogram regression in a fully observable series, with the corresponding gain in asymptotic efficiency and faster convergence over competitors. Local Wald, Lagrange Multiplier and Hausman type tests of the hypothesis of no correlation between the latent signal and noise are also proposed.
|Date of creation:||Apr 2010|
|Contact details of provider:|| Postal: Avda. Lehendakari, Aguirre, 83, 48015 Bilbao|
Phone: + 34 94 601 3740
Fax: + 34 94 601 4935
Web page: http://www.ea3.ehu.es
More information through EDIRC
|Order Information:|| Postal: Dpto. de Econometría y Estadística, Facultad de CC. Económicas y Empresariales, Universidad del País Vasco, Avda. Lehendakari Aguirre 83, 48015 Bilbao, Spain|
When requesting a correction, please mention this item's handle: RePEc:ehu:biltok:201004. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Alcira Macías)
If references are entirely missing, you can add them using this form.