Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data
Zudi Lu and
Econometrics and Statistics, 2017, vol. 2, issue C, 22-35
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.
Keywords: Irregularly spaced sampling locations; Large spatial time series data; Semiparametric spatio-temporal model and estimation; Spatial smoothing (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only. Contains open access articles
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:2:y:2017:i:c:p:22-35
Access Statistics for this article
Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi
More articles in Econometrics and Statistics from Elsevier
Series data maintained by Dana Niculescu ().