Indirect inference for locally stationary ARMA processes with stable innovations
dc.creator | Chou Chen, Shu Wei | |
dc.creator | Morettin, Pedro A. | |
dc.date.accessioned | 2023-04-19T19:32:45Z | |
dc.date.available | 2023-04-19T19:32:45Z | |
dc.date.issued | 2020 | |
dc.description.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 α-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 α-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. | es_ES |
dc.description.procedence | UCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Ciencias Económicas::Escuela de Estadística | es_ES |
dc.identifier.doi | 10.1080/00949655.2020.1797030 | |
dc.identifier.issn | 1563-5163 | |
dc.identifier.uri | https://hdl.handle.net/10669/89109 | |
dc.language.iso | eng | es_ES |
dc.rights | acceso abierto | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | Journal of Statistical Computation and Simulation, vol.90 (17), pp.1-31. | es_ES |
dc.subject | STATISTICAL INFERENCE | es_ES |
dc.subject | STATISTICS | es_ES |
dc.title | Indirect inference for locally stationary ARMA processes with stable innovations | es_ES |
dc.type | artículo original | es_ES |
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