Semiparametric time series models driven by latent factor

Gisele de Oliveira Maia, Wagner Barreto-Souza, Fernando de Souza Bastos, Hernando Ombao

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4 Scopus citations

Abstract

We introduce a class of semiparametric time series models (SemiParTS) driven by a latent factor process. The proposed SemiParTS class is flexible because, given the latent process, only the conditional mean and variance of the time series are specified. These are the primary features of SemiParTS: (i) no parametric form is assumed for the conditional distribution of the time series given the latent process; (ii) it is suitable for a wide range of data: non-negative, count, bounded, binary, and real-valued time series; (iii) it does not constrain the dispersion parameter to be known. The quasi-likelihood inference is employed in order to estimate the parameters in the mean function. Here, we derive explicit expressions for the marginal moments and for the autocorrelation function of the time series process so that a method of moments can be employed to estimate the dispersion parameter and also the parameters related to the latent process. Simulated results that aim to check the proposed estimation procedure are presented. Forecasting procedures are proposed and evaluated in simulated and real data. Analyses of the number of admissions in a hospital due to asthma and a total insolation time series illustrate the potential for practical situations that involve the proposed models.
Original languageEnglish (US)
JournalInternational Journal of Forecasting
DOIs
StatePublished - Feb 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-02-17
Acknowledgements: We would like to thank the Editors of the Special Issue 30 Years of Cointegration and Dynamic Factor Models Forecasting and its Future with Big Data and the anonymous Referee for the insightful and valuable comments and suggestions which led to a substantial improvement in the paper. G. Maia and W. Barreto-Souza would like to thank the financial support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq-Brazil, grant number 305543/2018-0). W. Barreto-Souza and H. Ombao would like to acknowledge the support for their work from the KAUST, Saudi Arabia Research Fund and NIH 1R01EB028753-01.

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