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Probit with Dependent Obervations


  • Poirier, Dale J.
  • Ruud, Paul A.


Estimation of limited dependent variable models with dependent observations has received relatively little attention due to the computational complexity of the maximum likelihood estimator. We develop a computationally attractive and relatively efficient estimator for this case that utilises the orthogonality conditions. The resulting Generalized Conditional Moment (GCM) estimators can be applied with a known or an unknown disturbance covariance matrix. Although the paper considers only the probit model, the approach is easily generalized to other limited dependent variable models.
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(This abstract was borrowed from another version of this item.)
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(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Poirier, Dale J. & Ruud, Paul A., 1987. "Probit with Dependent Obervations," Department of Economics, Working Paper Series qt04f5m9t2, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  • Handle: RePEc:cdl:econwp:qt04f5m9t2

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    References listed on IDEAS

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    1. Kan, Kamhon & Tsai, Wei-Der, 2005. "Parenting practices and children's education outcomes," Economics of Education Review, Elsevier, vol. 24(1), pages 29-43, February.
    2. Hajivassiliou, Vassilis A. & Ruud, Paul A., 1986. "Classical estimation methods for LDV models using simulation," Handbook of Econometrics,in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 40, pages 2383-2441 Elsevier.
    3. Leonie Sundmacher, 2012. "The effect of health shocks on smoking and obesity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(4), pages 451-460, August.
    4. Arturo Estrella & Anthony P. Rodrigues, 1998. "Consistent covariance matrix estimation in probit models with autocorrelated errors," Staff Reports 39, Federal Reserve Bank of New York.
    5. António R. Antunes & Diana Bonfim & Nuno Monteiro & Paulo M.M. Rodrigues, 2016. "Forecasting banking crises with dynamic panel probit models," Working Papers w201613, Banco de Portugal, Economics and Research Department.
    6. Federico Belotti & Giuseppe Ilardi, 2012. "Consistent Estimation of the “True” Fixed-effects Stochastic Frontier Model," CEIS Research Paper 231, Tor Vergata University, CEIS, revised 18 Apr 2012.
    7. Hausman, Jerry A. & Lo, Andrew W. & MacKinlay, A. Craig, 1992. "An ordered probit analysis of transaction stock prices," Journal of Financial Economics, Elsevier, vol. 31(3), pages 319-379, June.
    8. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    9. 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.
    10. de Jong, Robert M. & Woutersen, Tiemen, 2011. "Dynamic Time Series Binary Choice," Econometric Theory, Cambridge University Press, vol. 27(04), pages 673-702, August.
    11. Hautsch, Nikolaus & Klotz, Stefan, 2003. "Estimating the neighborhood influence on decision makers: theory and an application on the analysis of innovation decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 52(1), pages 97-113, September.
    12. Andrew Berg & Rebecca N. Coke, 2004. "Autocorrelation-Corrected Standard Errors in Panel Probits; An Application to Currency Crisis Prediction," IMF Working Papers 04/39, International Monetary Fund.
    13. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    14. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    15. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
    16. Michael J. Dueker & Katrin Wesche, 2001. "European business cycles: new indices and analysis of their synchronicity," Working Papers 1999-019, Federal Reserve Bank of St. Louis.
    17. 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.
    18. 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.
    19. Takeshima, Hiroyuki & Adhikari, Rajendra Prasad & Kumar, Anjani, 2016. "Is access to tractor service a binding constraint for Nepali Terai farmers? :," IFPRI discussion papers 1508, International Food Policy Research Institute (IFPRI).
    20. Julie Le Gallo, 2000. "Spatial econometrics (1, Spatial autocorrelation)
      [Econométrie spatiale (1, Autocorrélation spatiale)]
      ," Working Papers hal-01527290, HAL.
    21. Matias Costa Navajas & Aaron Thegeya, 2013. "Financial Soundness Indicators and Banking Crises," IMF Working Papers 13/263, International Monetary Fund.
    22. Vassilis A. Hajivassiliou, 1991. "Simulation Estimation Methods for Limited Dependent Variable Models," Cowles Foundation Discussion Papers 1007, Cowles Foundation for Research in Economics, Yale University.
    23. Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.
    24. Slade, Margaret E., 1999. "Sticky prices in a dynamic oligopoly: An investigation of (s,S) thresholds," International Journal of Industrial Organization, Elsevier, vol. 17(4), pages 477-511, May.


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