Publication

On model specification and parameter space definitions in higher order spatial econometric models

Elhorst, J. P., Lacombe, D. J. & Piras, G., Jan-2012, In : Regional Science and Urban Economics. 42, 1-2, p. 211-220 10 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Elhorst, J. P., Lacombe, D. J., & Piras, G. (2012). On model specification and parameter space definitions in higher order spatial econometric models. Regional Science and Urban Economics, 42(1-2), 211-220. https://doi.org/10.1016/j.regsciurbeco.2011.09.003

Author

Elhorst, J. Paul ; Lacombe, Donald J. ; Piras, Gianfranco. / On model specification and parameter space definitions in higher order spatial econometric models. In: Regional Science and Urban Economics. 2012 ; Vol. 42, No. 1-2. pp. 211-220.

Harvard

Elhorst, JP, Lacombe, DJ & Piras, G 2012, 'On model specification and parameter space definitions in higher order spatial econometric models', Regional Science and Urban Economics, vol. 42, no. 1-2, pp. 211-220. https://doi.org/10.1016/j.regsciurbeco.2011.09.003

Standard

On model specification and parameter space definitions in higher order spatial econometric models. / Elhorst, J. Paul; Lacombe, Donald J.; Piras, Gianfranco.

In: Regional Science and Urban Economics, Vol. 42, No. 1-2, 01.2012, p. 211-220.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Elhorst JP, Lacombe DJ, Piras G. On model specification and parameter space definitions in higher order spatial econometric models. Regional Science and Urban Economics. 2012 Jan;42(1-2):211-220. https://doi.org/10.1016/j.regsciurbeco.2011.09.003


BibTeX

@article{89205cfcb2ad4820836c2c4e919a9d11,
title = "On model specification and parameter space definitions in higher order spatial econometric models",
abstract = "Higher-order spatial econometric models that include more than one weights matrix have seen increasing use in the spatial econometrics literature. There are two distinct issues related to the specification of these extended models. The first issue is what form the higher-order spatial econometric model takes, i.e. higher-order polynomials in the spatial weights matrices vs. higher-order spatial autoregressive processes. The second issue relates to the parameter space in such models and how this can affect the choice of model specification, estimation, and inference. We outline a procedure that is simple both mathematically and computationally for finding the stationary region for spatial econometric models with up to K weights matrices for higher-order spatial autoregressive processes. We also compare and contrast this approach with the parameter space for models that incorporate higher-order polynomials in the spatial weights matrices. Regardless of the model utilized in empirical practice, ignoring the relevant parameter region can lead to incorrect inferences regarding both the nature of the spatial autocorrelation process and the effects of changes in covariates on the dependent variable. (C) 2011 Elsevier B.V. All rights reserved.",
keywords = "Higher order spatial models, Parameter space, Spatial econometrics, AUTOREGRESSIVE MODELS, YARDSTICK COMPETITION, AUTOCORRELATION, DISTURBANCES",
author = "Elhorst, {J. Paul} and Lacombe, {Donald J.} and Gianfranco Piras",
year = "2012",
month = jan,
doi = "10.1016/j.regsciurbeco.2011.09.003",
language = "English",
volume = "42",
pages = "211--220",
journal = "Regional Science and Urban Economics",
issn = "0166-0462",
publisher = "ELSEVIER SCIENCE BV",
number = "1-2",

}

RIS

TY - JOUR

T1 - On model specification and parameter space definitions in higher order spatial econometric models

AU - Elhorst, J. Paul

AU - Lacombe, Donald J.

AU - Piras, Gianfranco

PY - 2012/1

Y1 - 2012/1

N2 - Higher-order spatial econometric models that include more than one weights matrix have seen increasing use in the spatial econometrics literature. There are two distinct issues related to the specification of these extended models. The first issue is what form the higher-order spatial econometric model takes, i.e. higher-order polynomials in the spatial weights matrices vs. higher-order spatial autoregressive processes. The second issue relates to the parameter space in such models and how this can affect the choice of model specification, estimation, and inference. We outline a procedure that is simple both mathematically and computationally for finding the stationary region for spatial econometric models with up to K weights matrices for higher-order spatial autoregressive processes. We also compare and contrast this approach with the parameter space for models that incorporate higher-order polynomials in the spatial weights matrices. Regardless of the model utilized in empirical practice, ignoring the relevant parameter region can lead to incorrect inferences regarding both the nature of the spatial autocorrelation process and the effects of changes in covariates on the dependent variable. (C) 2011 Elsevier B.V. All rights reserved.

AB - Higher-order spatial econometric models that include more than one weights matrix have seen increasing use in the spatial econometrics literature. There are two distinct issues related to the specification of these extended models. The first issue is what form the higher-order spatial econometric model takes, i.e. higher-order polynomials in the spatial weights matrices vs. higher-order spatial autoregressive processes. The second issue relates to the parameter space in such models and how this can affect the choice of model specification, estimation, and inference. We outline a procedure that is simple both mathematically and computationally for finding the stationary region for spatial econometric models with up to K weights matrices for higher-order spatial autoregressive processes. We also compare and contrast this approach with the parameter space for models that incorporate higher-order polynomials in the spatial weights matrices. Regardless of the model utilized in empirical practice, ignoring the relevant parameter region can lead to incorrect inferences regarding both the nature of the spatial autocorrelation process and the effects of changes in covariates on the dependent variable. (C) 2011 Elsevier B.V. All rights reserved.

KW - Higher order spatial models

KW - Parameter space

KW - Spatial econometrics

KW - AUTOREGRESSIVE MODELS

KW - YARDSTICK COMPETITION

KW - AUTOCORRELATION

KW - DISTURBANCES

U2 - 10.1016/j.regsciurbeco.2011.09.003

DO - 10.1016/j.regsciurbeco.2011.09.003

M3 - Article

VL - 42

SP - 211

EP - 220

JO - Regional Science and Urban Economics

JF - Regional Science and Urban Economics

SN - 0166-0462

IS - 1-2

ER -

ID: 5496349