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A two-step approach to account for unobserved spatial heterogeneity

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  • Anna Gloria Billé
  • Roberto Benedetti
  • Paolo Postiglione

Abstract

A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.

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  • Anna Gloria Billé & Roberto Benedetti & Paolo Postiglione, 2017. "A two-step approach to account for unobserved spatial heterogeneity," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(4), pages 452-471, October.
    • Handle: RePEc:taf:specan:v:12:y:2017:i:4:p:452-471
    DOI: 10.1080/17421772.2017.1286373
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    Cited by:

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    2. Burhan Can Karahasan, 2020. "Winners and losers of rapid growth in Turkey: Analysis of the spatial variability of convergence," Papers in Regional Science, Wiley Blackwell, vol. 99(3), pages 603-644, June.
    3. Vidoli, Francesco & Pignataro, Giacomo & Benedetti, Roberto, 2022. "Identification of spatial regimes of the production function of Italian hospitals through spatially constrained cluster-wise regression," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    4. Philipp Otto & Osman Dou{g}an & Suleyman Tac{s}p{i}nar, 2022. "Dynamic Spatiotemporal ARCH Models," Papers 2202.13856, arXiv.org.
    5. Anna Gloria Billé & Leopoldo Catania, 2018. "Dynamic Spatial Autoregressive Models with Time-varying Spatial Weighting Matrices," BEMPS - Bozen Economics & Management Paper Series BEMPS55, Faculty of Economics and Management at the Free University of Bozen.
    6. Sabina Buczkowska & Nicolas Coulombel & Matthieu Lapparent, 2019. "A comparison of Euclidean Distance, Travel Times, and Network Distances in Location Choice Mixture Models," Networks and Spatial Economics, Springer, vol. 19(4), pages 1215-1248, December.
    7. Roy Cerqueti & Paolo Maranzano & Raffaele Mattera, 2025. "Spatially-Clustered Spatial Autoregressive Models with Application to Agricultural Market Concentration in Europe," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 431-465, June.
    8. Richard H. Rijnks & Stephen Sheppard, 2018. "Occupant Well-Being and House Values," Department of Economics Working Papers 2018-05, Department of Economics, Williams College.
    9. Luca Salvati & Margherita Carlucci, 2020. "Shaping Dimensions of Urban Complexity: The Role of Economic Structure and Socio-Demographic Local Contexts," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(1), pages 263-285, January.
    10. Bing Su & Fukang Zhu & Ke Zhu, 2023. "Statistical inference for the logarithmic spatial heteroskedasticity model with exogenous variables," Papers 2301.06658, arXiv.org.
    11. A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.
    12. Cavalieri, Marina & Di Caro, Paolo & Guccio, Calogero & Lisi, Domenico, 2020. "Does neighbours' grass matter? Testing spatial dependent heterogeneity in technical efficiency of Italian hospitals," Social Science & Medicine, Elsevier, vol. 265(C).

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