【光华讲坛】Dynamic Functional-coefficient Autoregressive Spatio-Temporal Models

主题Dynamic Functional-coefficient Autoregressive Spatio-Temporal Models

主讲人浙江大学数学学院 张荣茂教授

主持人js333线路登录 陈坤教授



主办单位:js333线路登录 科研处


张荣茂,现为浙江大学数学学院教授、浙大数据科学中心兼职教授、浙江大学统计所所长,浙江省现场统计研究所副理事长。2004年在浙江大学获得博士学位,2004年7月至2006年6月在北京大学从事博士后研究,2006年至今在浙江大学工作,多次访问香港科大、香港中文大学和伦敦政治经济学院。主要从事非平稳金融时间序列和高维空间计量经济模型的理论与应用研究,已发表SSCI/SCI论文40多篇,发表的杂志包括Annals of Statistics,Journal of the American Statistical Association,Journal of Econometrics,Econometric Theory, Journal of Business and Economic Statistics等统计与计量经济杂志。2015年获浙江省杰出青年基金,主持浙江省重点基金项目1项、国家自然科学基金和省部级基金项目多项,现任J. Korean Statist. Soc.等杂志的编委。


Nonlinear modelling of spatio-temporal data is often a challenge due to irregularly observed locations and location-wide non-stationarity. In this paper we propose a semiparametric family of Dynamic Functional-coefficient Autoregressive Spatio-Temporal (DyFAST) models to address the difficulties to overcome in modelling and analysis. The DyFAST models at least own two significant features. (i) The functional (or varying) coefficient structures that are popular in traditional statistical analysis of i.i.d. and time series data are extended to specify the autoregressive smooth coefficients depending both on a concerned regime and location. The DyFAST models can hence not only characterise the dynamic regime-switching nature but also adapt to the location-wide non-stationarity in real spatio-temporal data. (ii) 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 that is popular in spatial econometrics supposes that the weight matrix is pre-specified either by experts or by prior information of spatial locations. In practice, the weight matrix may be specified in different ways by data location features. Although model selection for an optimal weight matrix among the candidates is popular, it may suffer from loss of features of different weight matrices. 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 for estimation. Both theoretical properties and Monte Carlo simulations are investigated. The empirical application to an EU energy market dataset further demonstrates the usefulness with interesting findings by the DyFAST models.


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