IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v148y2016icp87-90.html
   My bibliography  Save this article

Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples

Author

Listed:
  • Mozharovskyi, Pavlo
  • Vogler, Jan

Abstract

Composite Marginal Likelihood (CML) has become a popular approach for estimating spatial probit models. However, for spatial autoregressive specifications the existing brute-force implementations are infeasible in large samples as they rely on inverting the high-dimensional precision matrix of the latent state variable. The contribution of this paper is to provide a CML implementation that circumvents inversion of that matrix and therefore can also be applied to very large sample sizes.

Suggested Citation

  • Mozharovskyi, Pavlo & Vogler, Jan, 2016. "Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples," Economics Letters, Elsevier, vol. 148(C), pages 87-90.
    • Handle: RePEc:eee:ecolet:v:148:y:2016:i:c:p:87-90
    DOI: 10.1016/j.econlet.2016.09.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016517651630372X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2016.09.022?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Roman Liesenfeld & Jean-François Richard & Jan Vogler, 2016. "Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables," Advances in Econometrics, in: Spatial Econometrics: Qualitative and Limited Dependent Variables, volume 37, pages 35-77, Emerald Group Publishing Limited.
    2. Wang, Honglin & Iglesias, Emma M. & Wooldridge, Jeffrey M., 2013. "Partial maximum likelihood estimation of spatial probit models," Journal of Econometrics, Elsevier, vol. 172(1), pages 77-89.
    3. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    4. Bhat, Chandra R., 2014. "The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models," Foundations and Trends(R) in Econometrics, now publishers, vol. 7(1), pages 1-117, July.
    5. 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.
    6. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    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. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    2. Chandra Bhat, 2015. "A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation," Transportation, Springer, vol. 42(5), pages 879-914, September.
    3. J. Paul Elhorst & Pim Heijnen & Anna Samarina & Jan P. A. M. Jacobs, 2017. "Transitions at Different Moments in Time: A Spatial Probit Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 422-439, March.
    4. 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.
    5. Heijnen, P. & Samarina, A.. & Jacobs, J.P.A.M. & Elhorst, J.P., 2013. "State transfers at different moments in time," Research Report 13006-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    6. Baltagi, Badi H. & Egger, Peter H. & Kesina, Michaela, 2017. "Determinants of firm-level domestic sales and exports with spillovers: Evidence from China," Journal of Econometrics, Elsevier, vol. 199(2), pages 184-201.
    7. Bhat, Chandra R. & Astroza, Sebastian & Hamdi, Amin S., 2017. "A spatial generalized ordered-response model with skew normal kernel error terms with an application to bicycling frequency," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 126-148.
    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. Chandra R. Bhat & Patrícia S. Lavieri, 2018. "A new mixed MNP model accommodating a variety of dependent non-normal coefficient distributions," Theory and Decision, Springer, vol. 84(2), pages 239-275, March.
    10. Sylvain Chareyron, 2016. "Le non-recours aux aides sociales sous conditions de ressources," Erudite Ph.D Dissertations, Erudite, number ph16-01 edited by Yannick L'Horty & François Legendre.
    11. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.
    12. repec:dgr:rugsom:13006-eef is not listed on IDEAS
    13. Mondal, Aupal & Bhat, Chandra R., 2022. "A spatial rank-ordered probit model with an application to travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 374-393.
    14. Elias, Wafa & Katoshevski-Cavari, Rachel, 2014. "The role of socio-economic and environmental characteristics in school-commuting behavior: A comparative study of Jewish and Arab children in Israel," Transport Policy, Elsevier, vol. 32(C), pages 79-87.
    15. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    16. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    17. Bhat, Chandra R. & Astroza, Sebastian & Sidharthan, Raghuprasad & Alam, Mohammad Jobair Bin & Khushefati, Waleed H., 2014. "A joint count-continuous model of travel behavior with selection based on a multinomial probit residential density choice model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 31-51.
    18. Asmussen, Katherine E. & Mondal, Aupal & Bhat, Chandra R., 2022. "Adoption of partially automated vehicle technology features and impacts on vehicle miles of travel (VMT)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 156-179.
    19. 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.
    20. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    21. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.

    More about this item

    Keywords

    Spatial probit models; Sparse matrices; Composite marginal likelihood; Partial maximum likelihood; Spatial econometrics;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:eee:ecolet:v:148:y:2016:i:c:p:87-90. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.