IDEAS home Printed from https://ideas.repec.org/p/boc/isug24/12.html
   My bibliography  Save this paper

Fitting spatial autoregressive logit and probit models using Stata: The spatbinary command

Author

Listed:
  • Daniele Spinelli

    (University of Milano-Bicocca)

Abstract

Spatial regressions can be estimated in Stata using the spregress, spxtregress, and spivregress commands. These commands allow users to Ft spatial autoregressive models in cross-sectional and panel data. They are designed to estimate regressions with continuous dependent variables. The spatbinary command now allows Stata users to Ft spatial logit and probit models, which are important models in applied econometrics.

Suggested Citation

  • Daniele Spinelli, 2024. "Fitting spatial autoregressive logit and probit models using Stata: The spatbinary command," Italian Stata Users' Group Meetings 2024 12, Stata Users Group.
    • Handle: RePEc:boc:isug24:12
    as

    Download full text from publisher

    File URL: http://repec.org/isug2024/Italy24_Spinelli.pdf
    File Function: presentation materials
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sylvain Weber & Martin Péclat, 2017. "A simple command to calculate travel distance and travel time," Stata Journal, StataCorp LLC, vol. 17(4), pages 962-971, December.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. Stephan Huber & Christoph Rust, 2016. "Calculate travel time and distance with OpenStreetMap data using the Open Source Routing Machine (OSRM)," Stata Journal, StataCorp LLC, vol. 16(2), pages 416-423, June.
    4. Klier, Thomas & McMillen, Daniel P, 2008. "Clustering of Auto Supplier Plants in the United States," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 460-471.
    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. Carrión-Flores, Carmen E. & Flores-Lagunes, Alfonso & Guci, Ledia, 2018. "An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 77-93.
    2. Martini, Gianmaria & Levaggi, Rosella & Spinelli, Daniele, 2022. "Is there a bias in patient choices for hospital care? Evidence from three Italian regional health systems," Health Policy, Elsevier, vol. 126(7), pages 668-679.
    3. Silveira Santos, Luís & Proença, Isabel, 2019. "The inversion of the spatial lag operator in binary choice models: Fast computation and a closed formula approximation," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 74-102.
    4. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    5. Songsermsawas, Tisorn & Baylis, Kathy & Chhatre, Ashwini & Michelson, Hope & Prasanna, Satya, 2015. "Friends or traders? Do social networks explain the use of market mechanisms by farmers in India," 2015 Conference, August 9-14, 2015, Milan, Italy 211206, International Association of Agricultural Economists.
    6. Chuanmin Zhao & Xi Qu, 2022. "Social networks and internal migration in China: A spatial autoregressive model," Review of Development Economics, Wiley Blackwell, vol. 26(2), pages 1132-1163, May.
    7. Brasington, David & Flores-Lagunes, Alfonso & Guci, Ledia, 2016. "A spatial model of school district open enrollment choice," Regional Science and Urban Economics, Elsevier, vol. 56(C), pages 1-18.
    8. Anna Gloria Billé, 2013. "Computational Issues in the Estimation of the Spatial Probit Model: A Comparison of Various Estimators," The Review of Regional Studies, Southern Regional Science Association, vol. 43(2,3), pages 131-154, Winter.
    9. Kelchtermans, Stijn & Neicu, Daniel & Teirlinck, Peter, 2020. "The role of peer effects in firms’ usage of R&D tax exemptions," Journal of Business Research, Elsevier, vol. 108(C), pages 74-91.
    10. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian estimation of multivariate panel probits with higher‐order network interdependence and an application to firms' global market participation in Guangdong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1356-1378, November.
    11. Badi H. Baltagi & Ying Deng & Xiangjun Ma, 2018. "Network effects on labor contracts of internal migrants in China: a spatial autoregressive model," Empirical Economics, Springer, vol. 55(1), pages 265-296, August.
    12. Jong Wook Lee & So Young Sohn, 2021. "Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-11, December.
    13. David J. Lewis & Bradford L. Barham & Brian Robinson, 2011. "Are There Spatial Spillovers in the Adoption of Clean Technology? The Case of Organic Dairy Farming," Land Economics, University of Wisconsin Press, vol. 87(2), pages 250-267.
    14. Anna Gloria Billé & Samantha Leorato, 2017. "Quasi-ML estimation, Marginal Effects and Asymptotics for Spatial Autoregressive Nonlinear Models," BEMPS - Bozen Economics & Management Paper Series BEMPS44, Faculty of Economics and Management at the Free University of Bozen.
    15. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2018. "Generalized spatial autocorrelation in a panel-probit model with an application to exporting in China," Empirical Economics, Springer, vol. 55(1), pages 193-211, August.
    16. Alisson Castro Barreto & Tailon Martins & Stéfane Dias Rodrigues & Adriano Mendonça Souza, 2022. "Ecological study of mortality by prostate and breast cancer in Brazil," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(2), pages 495-509, April.
    17. Ina Blind & Matz Dahlberg & Gustav Engström & John Östh, 2018. "Construction of Register-based Commuting Measures," CESifo Economic Studies, CESifo Group, vol. 64(2), pages 292-326.
    18. Piras, Gianfranco & Sarrias, Mauricio, 2023. "One or two-step? Evaluating GMM efficiency for spatial binary probit models," Journal of choice modelling, Elsevier, vol. 48(C).
    19. Mark J Holmes & Jesús Otero & Theodore Panagiotidis, 2018. "Climbing the property ladder: An analysis of market integration in London property prices," Urban Studies, Urban Studies Journal Limited, vol. 55(12), pages 2660-2681, September.
    20. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2020. "Treatment Effects With Heterogeneous Externalities," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 826-838, October.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:boc:isug24:12. 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: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/stataea.html .

    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.