On model fitting and estimation of strictly stationary processes

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openAccess

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Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2017

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Mcode

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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.

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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