An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions

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Abstract

Economic problems that require micro-level analysis within a discrete-choice framework are often spatial processes, where standard models result in inconsistent parameter estimates. Existing methods for spatial discrete-choice models become infeasible in micro-level data applications due to large sample sizes. We propose an extension of Klier and McMillen's (2008) generalized moments estimator to multinomial choice models with spatial lag dependence that is computationally simple. Simulations indicate that the proposed estimator captures accurately the degree of spatial dependence in the data, provided spatial dependence is not too high. The methodology is employed to analyze the conversion process to various land uses using parcel-level data from a rural-urban fringe county within a large metropolitan area. We find evidence of positive spatial dependence of about 0.36—a result consistent with the widely-accepted idea that land-use conversion is a spatial process. This suggests that uncoordinated local land-use policies designed at a small scale, while attempting to manage growth at a local level, may fragment urban development and result in suboptimal land-use patterns at a regional level.

Original languageEnglish (US)
Pages (from-to)77-93
Number of pages17
JournalRegional Science and Urban Economics
Volume69
DOIs
StatePublished - Mar 1 2018

Fingerprint

land use
micro level
spatial data
urban development
metropolitan area
agglomeration area
simulation
methodology
Discrete choice models
Land use
Estimator
Spatial dependence
Spatial lag
economics
evidence
Urban development
Sample size
Simulation
Metropolitan areas
Economics

Keywords

  • Land-use policy
  • Spatial lag dependence
  • Spatial multinomial choice model

ASJC Scopus subject areas

  • Economics and Econometrics
  • Urban Studies

Cite this

@article{787281b0b6a04e9ab40dfd3681de00bb,
title = "An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions",
abstract = "Economic problems that require micro-level analysis within a discrete-choice framework are often spatial processes, where standard models result in inconsistent parameter estimates. Existing methods for spatial discrete-choice models become infeasible in micro-level data applications due to large sample sizes. We propose an extension of Klier and McMillen's (2008) generalized moments estimator to multinomial choice models with spatial lag dependence that is computationally simple. Simulations indicate that the proposed estimator captures accurately the degree of spatial dependence in the data, provided spatial dependence is not too high. The methodology is employed to analyze the conversion process to various land uses using parcel-level data from a rural-urban fringe county within a large metropolitan area. We find evidence of positive spatial dependence of about 0.36—a result consistent with the widely-accepted idea that land-use conversion is a spatial process. This suggests that uncoordinated local land-use policies designed at a small scale, while attempting to manage growth at a local level, may fragment urban development and result in suboptimal land-use patterns at a regional level.",
keywords = "Land-use policy, Spatial lag dependence, Spatial multinomial choice model",
author = "{Carrion Flores}, Carmen and Alfonso Flores-Lagunes and Ledia Guci",
year = "2018",
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doi = "10.1016/j.regsciurbeco.2017.12.005",
language = "English (US)",
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journal = "Regional Science and Urban Economics",
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TY - JOUR

T1 - An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions

AU - Carrion Flores, Carmen

AU - Flores-Lagunes, Alfonso

AU - Guci, Ledia

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Economic problems that require micro-level analysis within a discrete-choice framework are often spatial processes, where standard models result in inconsistent parameter estimates. Existing methods for spatial discrete-choice models become infeasible in micro-level data applications due to large sample sizes. We propose an extension of Klier and McMillen's (2008) generalized moments estimator to multinomial choice models with spatial lag dependence that is computationally simple. Simulations indicate that the proposed estimator captures accurately the degree of spatial dependence in the data, provided spatial dependence is not too high. The methodology is employed to analyze the conversion process to various land uses using parcel-level data from a rural-urban fringe county within a large metropolitan area. We find evidence of positive spatial dependence of about 0.36—a result consistent with the widely-accepted idea that land-use conversion is a spatial process. This suggests that uncoordinated local land-use policies designed at a small scale, while attempting to manage growth at a local level, may fragment urban development and result in suboptimal land-use patterns at a regional level.

AB - Economic problems that require micro-level analysis within a discrete-choice framework are often spatial processes, where standard models result in inconsistent parameter estimates. Existing methods for spatial discrete-choice models become infeasible in micro-level data applications due to large sample sizes. We propose an extension of Klier and McMillen's (2008) generalized moments estimator to multinomial choice models with spatial lag dependence that is computationally simple. Simulations indicate that the proposed estimator captures accurately the degree of spatial dependence in the data, provided spatial dependence is not too high. The methodology is employed to analyze the conversion process to various land uses using parcel-level data from a rural-urban fringe county within a large metropolitan area. We find evidence of positive spatial dependence of about 0.36—a result consistent with the widely-accepted idea that land-use conversion is a spatial process. This suggests that uncoordinated local land-use policies designed at a small scale, while attempting to manage growth at a local level, may fragment urban development and result in suboptimal land-use patterns at a regional level.

KW - Land-use policy

KW - Spatial lag dependence

KW - Spatial multinomial choice model

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