IDEAS home Printed from https://ideas.repec.org/a/sae/inrsre/v47y2024i2p204-226.html
   My bibliography  Save this article

Using Web-Data to Estimate Spatial Regression Models

Author

Listed:
  • Giuseppe Arbia
  • Vincenzo Nardelli

Abstract

Macro econometrics has been recently affected by the so-called ‘Google Econometrics’. Comparatively less attention has been paid to the subject by the regional and spatial sciences where the Big Data revolution is challenging the conventional econometric techniques with the availability of a variety of non- traditionally collected data (such as, e. g., crowdsourcing, web scraping, etc) which are almost invariably geo-coded. However, these unconventionally collected data represent only what in statistics is known as a “convenience sample†that does not allow any sound probabilistic inference. This paper aims at making aware researchers of the consequence of the unwise use of such data in the applied work and to propose a technique to minimize such the negative effects in the estimation of spatial regression. The method consists of manipulating the data prior their use in an inferential context.

Suggested Citation

  • Giuseppe Arbia & Vincenzo Nardelli, 2024. "Using Web-Data to Estimate Spatial Regression Models," International Regional Science Review, , vol. 47(2), pages 204-226, March.
    • Handle: RePEc:sae:inrsre:v:47:y:2024:i:2:p:204-226
    DOI: 10.1177/01600176231173438
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/01600176231173438
    Download Restriction: no
    File URL: https://libkey.io/10.1177/01600176231173438?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Werner G. Müller, 2007. "Collecting Spatial Data," Springer Books, Springer, edition 0, number 978-3-540-31175-1, February.
    2. Scott Loveridge & Dusan Paredes, 2018. "Are Rural Costs of Living Lower? Evidence from a Big Mac Index Approach," International Regional Science Review, , vol. 41(3), pages 364-382, May.
    3. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    4. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    5. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    6. Shu Yang & Jae Kwang Kim & Rui Song, 2020. "Doubly robust inference when combining probability and non‐probability samples with high dimensional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(2), pages 445-465, April.
    7. Boeing, Geoff, 2017. "New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings," SocArXiv v54w4, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fernando Alvarez & Francesco Lippi & Juan Passadore, 2017. "Are State- and Time-Dependent Models Really Different?," NBER Macroeconomics Annual, University of Chicago Press, vol. 31(1), pages 379-457.
    2. Sandy Fréret & Denis Maguain, 2017. "The effects of agglomeration on tax competition: evidence from a two-regime spatial panel model on French data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(6), pages 1100-1140, December.
    3. Yong Bao & Xiaotian Liu & Lihong Yang, 2020. "Indirect Inference Estimation of Spatial Autoregressions," Econometrics, MDPI, vol. 8(3), pages 1-26, September.
    4. Vicente Rios Ibañez, 2014. "What drives regional unemployment convergence?," ERSA conference papers ersa14p924, European Regional Science Association.
    5. Nabeel Al-Milli & Amjad Hudaib & Nadim Obeid, 2021. "Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem," Mathematics, MDPI, vol. 9(8), pages 1-18, April.
    6. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
    7. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    8. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    9. Théophile Azomahou, 2008. "Minimum distance estimation of the spatial panel autoregressive model," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 2(1), pages 49-83, April.
    10. Badi H. Baltagi & Zhenlin Yang, 2013. "Standardized LM tests for spatial error dependence in linear or panel regressions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 103-134, February.
    11. Tizheng Li & Xiaojuan Kang, 2022. "Variable selection of higher-order partially linear spatial autoregressive model with a diverging number of parameters," Statistical Papers, Springer, vol. 63(1), pages 243-285, February.
    12. Jennifer Peña & Elvira Prades, 2021. "Price setting in Chile: Micro evidence from consumer on-line prices during the social outbreak and Covid-19," Working Papers 2112, Banco de España.
    13. Lee, Jungyoon & Robinson, Peter M., 2016. "Series estimation under cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 190(1), pages 1-17.
    14. Lee, Jiyon, 2018. "A spatial latent class model," Economics Letters, Elsevier, vol. 162(C), pages 62-68.
    15. Ariane Amin & Johanna Choumert, 2015. "Development and biodiversity conservation in Sub-Saharan Africa: A spatial analysis," Economics Bulletin, AccessEcon, vol. 35(1), pages 729-744.
    16. Shi, Wei & Lee, Lung-fei, 2018. "A spatial panel data model with time varying endogenous weights matrices and common factors," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 6-34.
    17. Li, Kunpeng, 2017. "Fixed-effects dynamic spatial panel data models and impulse response analysis," Journal of Econometrics, Elsevier, vol. 198(1), pages 102-121.
    18. Orkideh Gharehgozli & Vidya Atal, 2019. """Big Mac Real"" Income Inequality : A Multinational Study," LIS Working papers 775, LIS Cross-National Data Center in Luxembourg.
    19. Zhang Yuanqing, 2014. "Estimation of Partially Specified Spatial Autoregressive Model," Journal of Systems Science and Information, De Gruyter, vol. 2(3), pages 226-235, June.
    20. Markus Dertwinkel-Kalt & Mats Köster, 2017. "Salience and Online Sales: The Role of Brand Image Concerns," CESifo Working Paper Series 6787, CESifo.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:inrsre:v:47:y:2024:i:2:p:204-226. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.