On model fitting and estimation of strictly stationary processes

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2017
Major/Subject
Mcode
Degree programme
Language
en
Pages
381-406
Series
Modern Stochastics: Theory and Applications, Volume 4, issue 4
Abstract
Stationary processes have been extensively studied in the literature. Their applications include modeling and forecasting numerous real life phenomena such as natural disasters, sales and market movements. When stationary processes are considered, modeling is traditionally based on fitting an autoregressive moving average (ARMA) process. However, we challenge this conventional approach. Instead of fitting an ARMA model, we apply an AR(1) characterization in modeling any strictly stationary processes. Moreover, we derive consistent and asymptotically normal estimators of the corresponding model parameter.
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Keywords
representation, asymptotic normality, consistency, estimation, strictly stationary processes
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Citation
Voutilainen , M , Viitasaari , L & Ilmonen , P 2017 , ' On model fitting and estimation of strictly stationary processes ' , Modern Stochastics: Theory and Applications , vol. 4 , no. 4 , pp. 381-406 . https://doi.org/10.15559/17-VMSTA91