Artikel

Fisher's z distribution-based mixture autoregressive model

We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher's z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series. The model consists of a mixture of K-component Fisher's z autoregressive models with the mixing proportions changing over time. This model can capture time series with both heteroskedasticity and multimodal conditional distribution, using Fisher's z distribution as an innovation in the MAR model. The ZMAR model is classified as nonlinearity in the level (or mode) model because the mode of the Fisher's z distribution is stable in its location parameter, whether symmetric or asymmetric. Using the Markov Chain Monte Carlo (MCMC) algorithm, e.g., the No-U-Turn Sampler (NUTS), we conducted a simulation study to investigate the model performance compared to the GMAR model and Student t Mixture Autoregressive (TMAR) model. The models are applied to the daily IBM stock prices and the monthly Brent crude oil prices. The results show that the proposed model outperforms the existing ones, as indicated by the Pareto-Smoothed Important Sampling Leave-One-Out cross-validation (PSIS-LOO) minimum criterion.

Language
Englisch

Bibliographic citation
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 3 ; Pages: 1-35 ; Basel: MDPI

Classification
Wirtschaft
Subject
Bayesian analysis
Fisher's z distribution
mixture autoregressive model
no-U-turn sampler
Stan program
the Brent crude oil prices
the IBM stock prices

Event
Geistige Schöpfung
(who)
Solikhah, Arifatus
Kuswanto, Heri
Iriawan, Nur
Fithriasari, Kartika
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2021

DOI
doi:10.3390/econometrics9030027
Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Solikhah, Arifatus
  • Kuswanto, Heri
  • Iriawan, Nur
  • Fithriasari, Kartika
  • MDPI

Time of origin

  • 2021

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