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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Indirect inference for locally stationary ARMA processes with stable innovations

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Author(s):
Chou-Chen, Shu Wei [1] ; Morettin, Pedro A. [1]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Inst Math & Stat, Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; JUL 2020.
Web of Science Citations: 0
Abstract

The class of locally stationary processes assumes that there is a time-varying spectral representation, that is, the existence of finite second moment. We propose the alpha-stable locally stationary process by modifying the innovations into stable distributions and the indirect inference to estimate this type of model. Due to the infinite variance, some of interesting properties such as time-varying autocorrelation cannot be defined. However, since the alpha-stable family of distributions is closed under linear combination which includes the possibility of handling asymmetry and thicker tails, the proposed model has the same tail behaviour throughout the time. In this paper, we propose this new model, present theoretical properties of the process and carry out simulations related to the indirect inference in order to estimate the parametric form of the model. Finally, an empirical application is illustrated. (AU)

FAPESP's process: 18/04654-9 - Time series, wavelets and high dimensional data
Grantee:Pedro Alberto Morettin
Support type: Research Projects - Thematic Grants