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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.

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

Identifiers

Local EPrints ID: 426419
URI: http://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

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Date deposited: 27 Nov 2018 17:30
Last modified: 16 Mar 2024 07:20

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Contributors

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

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