The University of Southampton
University of Southampton Institutional Repository

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

Estimation for semiparametric nonlinear regression of irregularly located spatial time-series data
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 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.
2452-3062
22-35
Al-Sulami, Dawlah
26e4d5d8-a745-4e49-a133-a6cd7e9b9656
Jiang, Zhenyu
940e16c6-ad5d-49c3-be60-a1595224d77e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Zhu, Jun
e25fa87d-445b-4bb8-b15f-1e608b4d8b1c
Al-Sulami, Dawlah
26e4d5d8-a745-4e49-a133-a6cd7e9b9656
Jiang, Zhenyu
940e16c6-ad5d-49c3-be60-a1595224d77e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Zhu, Jun
e25fa87d-445b-4bb8-b15f-1e608b4d8b1c

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, 2, 22-35. (doi:10.1016/j.ecosta.2017.01.002).

Record type: Article

Abstract

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.

Text non-Blinded manuscript-2.pdf - Accepted Manuscript
Download (2MB)

More information

Accepted/In Press date: 3 January 2017
e-pub ahead of print date: 23 January 2017
Published date: April 2017
Organisations: Statistics

Identifiers

Local EPrints ID: 404649
URI: https://eprints.soton.ac.uk/id/eprint/404649
ISSN: 2452-3062
PURE UUID: c14f16fe-18fb-4e3d-8723-d93ba220823e

Catalogue record

Date deposited: 13 Jan 2017 16:36
Last modified: 30 Apr 2018 04:01

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×