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A Conditionally Parametric Probit Model Of Microdata Land Use In Chicago

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  • Daniel McMillen
  • Maria Edisa Soppelsa

Abstract

type="main"> Spatial data sets pose challenges for discrete choice models because the data are unlikely to be independently and identically distributed. A conditionally parametric spatial probit model is amenable to very large data sets while imposing far less structure on the data than conventional parametric models. We illustrate the approach using data on 474,170 individual lots in the City of Chicago. The results suggest that simple functional forms are not appropriate for explaining the spatial variation in residential land use across the entire city.

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  • Daniel McMillen & Maria Edisa Soppelsa, 2015. "A Conditionally Parametric Probit Model Of Microdata Land Use In Chicago," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 391-415, June.
    • Handle: RePEc:bla:jregsc:v:55:y:2015:i:3:p:391-415
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    File URL: http://hdl.handle.net/10.1111/jors.12174
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    References listed on IDEAS

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    1. Daniel P. McMillen & Christian L. Redfearn, 2010. "Estimation And Hypothesis Testing For Nonparametric Hedonic House Price Functions," Journal of Regional Science, Wiley Blackwell, vol. 50(3), pages 712-733, August.
    2. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
    3. Richard Meese & Nancy Wallace, 1991. "Nonparametric Estimation of Dynamic Hedonic Price Models and the Construction of Residential Housing Price Indices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 19(3), pages 308-332, September.
    4. Daniel P. McMillen, 2012. "Perspectives On Spatial Econometrics: Linear Smoothing With Structured Models," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 192-209, May.
    5. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    6. James P. LeSage & R. Kelley Pace & Nina Lam & Richard Campanella & Xingjian Liu, 2011. "New Orleans business recovery in the aftermath of Hurricane Katrina," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 1007-1027, October.
    7. Wrenn, Douglas H. & Sam, Abdoul G., 2014. "Geographically and temporally weighted likelihood regression: Exploring the spatiotemporal determinants of land use change," Regional Science and Urban Economics, Elsevier, vol. 44(C), pages 60-74.
    8. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

    1. Xian F. Bak & Geoffrey J. D. Hewings, 2019. "The heterogeneous spatial impact of foreclosures on nearby property values," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 439-466, June.
    2. Ghislain Geniaux, 2024. "Speeding up estimation of spatially varying coefficients models," Journal of Geographical Systems, Springer, vol. 26(3), pages 293-327, July.
    3. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    4. Fu, Xiaowen & Jin, Huan & Liu, Shaoxuan & Oum, Tae H. & Yan, Jia, 2019. "Exploring network effects of point-to-point networks: An investigation of the spatial patterns of Southwest Airlines’ network," Transport Policy, Elsevier, vol. 76(C), pages 36-45.
    5. McMillen, Daniel, 2015. "Conditionally parametric quantile regression for spatial data: An analysis of land values in early nineteenth century Chicago," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 28-38.

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