On model fitting and estimation of strictly stationary processes
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2017
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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.Description
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