The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics

Sergio J. Rey, Wei Kang, Levi Wolf

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series.

Original languageEnglish (US)
Pages (from-to)377-398
Number of pages22
JournalJournal of Geographical Systems
Volume18
Issue number4
DOIs
StatePublished - Oct 1 2016

Fingerprint

income distribution
Markov chain
regional distribution
effect
test
popularity
time series
spatial distribution
simulation
ability

Keywords

  • Convergence
  • Distributional dynamics
  • Growth
  • Spatial dependence

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Cite this

The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics. / Rey, Sergio J.; Kang, Wei; Wolf, Levi.

In: Journal of Geographical Systems, Vol. 18, No. 4, 01.10.2016, p. 377-398.

Research output: Contribution to journalArticle

@article{dad2a4aeae7e405daa1f7e57a5956674,
title = "The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics",
abstract = "Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series.",
keywords = "Convergence, Distributional dynamics, Growth, Spatial dependence",
author = "Rey, {Sergio J.} and Wei Kang and Levi Wolf",
year = "2016",
month = "10",
day = "1",
doi = "10.1007/s10109-016-0234-x",
language = "English (US)",
volume = "18",
pages = "377--398",
journal = "Journal of Geographical Systems",
issn = "1435-5930",
publisher = "Springer Verlag",
number = "4",

}

TY - JOUR

T1 - The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics

AU - Rey, Sergio J.

AU - Kang, Wei

AU - Wolf, Levi

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series.

AB - Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series.

KW - Convergence

KW - Distributional dynamics

KW - Growth

KW - Spatial dependence

UR - http://www.scopus.com/inward/record.url?scp=84986256199&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84986256199&partnerID=8YFLogxK

U2 - 10.1007/s10109-016-0234-x

DO - 10.1007/s10109-016-0234-x

M3 - Article

AN - SCOPUS:84986256199

VL - 18

SP - 377

EP - 398

JO - Journal of Geographical Systems

JF - Journal of Geographical Systems

SN - 1435-5930

IS - 4

ER -