The University of Southampton
University of Southampton Institutional Repository

On a semiparametric data-driven nonlinear model with penalized spatio-temporal lag interactions

On a semiparametric data-driven nonlinear model with penalized spatio-temporal lag interactions
On a semiparametric data-driven nonlinear model with penalized spatio-temporal lag interactions
To study possibly nonlinear relationship between housing price index and consumer price index for individual states in the US, accounting for the temporal lag interactions of the housing price in a given state and spatio-temporal lag interactions between states could improve the accuracy of estimation and forecasting. There lacks, however, methodology to objectively identify and
estimate such spatio-temporal lag interactions. In this paper, we propose a semiparametric data-driven nonlinear time series regression method that accounts for lag interactions across space and over time. A penalized procedure utilizing adaptive Lasso is developed for the identification and estimation of important spatio-temporal lag interactions. Theoretical properties for our proposed methodology are established under a general near epoch dependence structure and thus the results can be applied to a variety of linear and nonlinear time series processes. For illustration, we analyze the US housing price data and demonstrate substantial improvement in forecasting via the identification of nonlinear relationship between housing price index and consumer price index as
well as spatio-temporal lag interactions.
0143-9782
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 (2018) On a semiparametric data-driven nonlinear model with penalized spatio-temporal lag interactions. Journal of Time Series Analysis. (doi:10.1111/jtsa.12442).

Record type: Article

Abstract

To study possibly nonlinear relationship between housing price index and consumer price index for individual states in the US, accounting for the temporal lag interactions of the housing price in a given state and spatio-temporal lag interactions between states could improve the accuracy of estimation and forecasting. There lacks, however, methodology to objectively identify and
estimate such spatio-temporal lag interactions. In this paper, we propose a semiparametric data-driven nonlinear time series regression method that accounts for lag interactions across space and over time. A penalized procedure utilizing adaptive Lasso is developed for the identification and estimation of important spatio-temporal lag interactions. Theoretical properties for our proposed methodology are established under a general near epoch dependence structure and thus the results can be applied to a variety of linear and nonlinear time series processes. For illustration, we analyze the US housing price data and demonstrate substantial improvement in forecasting via the identification of nonlinear relationship between housing price index and consumer price index as
well as spatio-temporal lag interactions.

Text
revision_R2_v2 - Accepted Manuscript
Restricted to Repository staff only until 7 November 2019.
Request a copy

More information

Accepted/In Press date: 7 November 2018
e-pub ahead of print date: 18 December 2018

Identifiers

Local EPrints ID: 426419
URI: https://eprints.soton.ac.uk/id/eprint/426419
ISSN: 0143-9782
PURE UUID: a3acb4ce-5d17-454e-a739-91495e0350fa
ORCID for Zudi Lu: ORCID iD orcid.org/0000-0003-0893-832X

Catalogue record

Date deposited: 27 Nov 2018 17:30
Last modified: 13 Jun 2019 00:31

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.

×