A0884
Title: On dynamic functional-coefficient autoregressive spatio-temporal models with irregular location wide nonstationarity
Authors: Zudi Lu - University of Southampton (United Kingdom) [presenting]
Xiaohang Ren - Central South University (China)
Rongmao Zhang - Zhejiang University (China)
Abstract: Nonlinear modelling of spatio-temporal data is often a challenge due to irregularly observed locations and location-wide non-stationarity. We propose a semiparametric family of Dynamic Functional-coefficient Autoregressive Spatio-Temporal (DyFAST) models to address the difficulties. First, we specify the dynamic autoregressive smooth coefficients depending on both a concerned regime and location so that the models can characterise not only the dynamic regime-switching nature but also the location-wide non-stationarity in real spatio-temporal data. Second, two semiparametric smoothing schemes are proposed to model the dynamic neighbouring-time interaction effects with irregular locations incorporated by (spatial) weight matrices. The first scheme popular in econometrics supposes that the weight matrix is pre-specified. In practice, many weight matrices can be generated differently by data location features. Model selection for an optimal one is popular but may suffer from a loss of features of different candidates. Our second scheme is thus to suggest a weight matrix fusion to let data combine or select the candidates. Accordingly, different semiparametric smoothing procedures are developed. Both theoretical properties and Monte Carlo simulations are investigated. The empirical application to an EU energy market dataset further demonstrates the usefulness of our DyFAST models.
