Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations

• Published in: Journal of applied statistics Vol. 46; no. 11; pp. 2010 - 2029
• Main Authors: Hajrajabi, A., Maleki, M.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 18.08.2019; Taylor & Francis Ltd
• Subjects: 62-07; Algorithms; Autoregressive models; Computer simulation; Datasets; Economic models; EM algorithm; Innovations; mixture models; nonlinear autoregressive models; Regression analysis; scale mixtures of skew normal distributions; semiparametric estimation; Skewed distributions; Statistical methods
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2019.1575953

We propose data generating structures which can be represented as the nonlinear autoregressive models with single and finite mixtures of scale mixtures of skew normal innovations. This class of models covers symmetric/asymmetric and light/heavy-tailed distributions, so provide a useful generalization of the symmetrical nonlinear autoregressive models. As semiparametric and nonparametric curve estimation are the approaches for exploring the structure of a nonlinear time series data set, in this article the semiparametric estimator for estimating the nonlinear function of the model is investigated based on the conditional least square method and nonparametric kernel approach. Also, an Expectation-Maximization-type algorithm to perform the maximum likelihood (ML) inference of unknown parameters of the model is proposed. Furthermore, some strong and weak consistency of the semiparametric estimator in this class of models are presented. Finally, to illustrate the usefulness of the proposed model, some simulation studies and an application to real data set are considered.