RT Journal Article
T1 Frequency domain minimum distance inference for possibly noninvertible and noncausal arma models.
A1 Velasco, Carlos
A1 Lobato García-Miján, Ignacio
AB This article introduces frequency domain minimum distance procedures for performing inference in general, possibly non causal and/or noninvertible, autoregressive moving average (ARMA) models. We use information from higher order moments to achieve identification on the location of the roots of the AR and MA polynomials for non-Gaussian time series. We propose a minimum distance estimator that optimally combines the information contained in second, third, and fourth moments. Contrary to existing estimators, the proposed one is consistent under general assumptions, and may improve on the efficiency of estimators based on only second order moments. Our procedures are also applicable for processes for which either the third or the fourth order spectral density is the zero function.
PB Institute of Mathematical Statistics
SN 0090-5364
YR 2018
FD 2018-04-01
LK https://hdl.handle.net/10016/34510
UL https://hdl.handle.net/10016/34510
LA eng
NO Supported by Ministerio Economía y Competitividad (Spain), Grants ECO2012-31748,ECO2014-57007p and MDM 2014-0431, and Comunidad de Madrid, MadEco-CM (S2015/HUM-3444).Supported by Asociación Mexicana de Cultura and from the Mexican Consejo Nacional de Cienciay Tecnología (CONACYT) under project Grant 151624.
DS e-Archivo
RD 1 sept. 2024