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Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data

Al-Sulami, Dawlah, Jiang, Zhenyu, Lu, Zudi and Zhu, Jun (2017) Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data Econometrics and Statistics, pp. 1-36.

Record type: Article


Large spatial time-series data with complex structures collected at irregularly spaced sampling locations are prevalent in a wide range of applications. However, econometric and statistical methodology for nonlinear modeling and analysis of such data remains rare. A
semiparametric nonlinear regression is thus proposed for modelling nonlinear relationship between response and covariates, which is location-based and considers both temporal-lag and spatial-neighbouring effects, allowing data-generating process nonstationary over space (but
turned into stationary series along time) while the sampling spatial grids can be irregular. A semiparametric method for estimation is also developed that is computationally feasible and thus enables application in practice. Asymptotic properties of the proposed estimators are established while numerical simulations are carried for comparisons between estimates before and after spatial smoothing. Empirical application to investigation of housing prices in relation to interest rates in the United States is demonstrated, with a nonlinear threshold structure identified.

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Accepted/In Press date: 3 January 2017
Organisations: Statistics


Local EPrints ID: 404649
ISSN: 2452-3062
PURE UUID: c14f16fe-18fb-4e3d-8723-d93ba220823e

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Date deposited: 13 Jan 2017 16:36
Last modified: 17 Jul 2017 17:31

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Author: Dawlah Al-Sulami
Author: Zhenyu Jiang
Author: Zudi Lu
Author: Jun Zhu

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