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

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 modeling nonlinear relationship between response and covariates, which is location-based and considers both temporal-lag and spatial-neighboring 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.