One or two-step? Evaluating GMM efficiency for spatial binary probit models
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DOI: 10.1016/j.jocm.2023.100432
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References listed on IDEAS
- 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.
- Harry H. Kelejian & Ingmar R. Prucha, 1997. "A Generalized Spatial Two Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," Electronic Working Papers 97-002, University of Maryland, Department of Economics, revised Aug 1997.
- 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.
- Luís Silveira Santos & Isabel Proença, 2017. "The Inversion of the Spatial Lag Operator in Binary Choice Models: Fast Computation and a Closed Formula Approximation," Working Papers REM 2017/11, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
- Chesher, Andrew & Irish, Margaret, 1987. "Residual analysis in the grouped and censored normal linear model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 33-61.
- Kurt J. Beron & Wim P. M. Vijverberg, 2004. "Probit in a Spatial Context: A Monte Carlo Analysis," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 8, pages 169-195, Springer.
- James P. LeSage & R. Kelley Pace & Nina Lam & Richard Campanella & Xingjian Liu, 2011. "New Orleans business recovery in the aftermath of Hurricane Katrina," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 1007-1027, October.
- Anping Chen & Marlon Boarnet & Mark Partridge & Raffaella Calabrese & Johan A. Elkink, 2014.
"Estimators Of Binary Spatial Autoregressive Models: A Monte Carlo Study,"
Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 664-687, September.
- Raffaella Calabrese & Johan A. Elkink, 2012. "Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study," Working Papers 201215, Geary Institute, University College Dublin.
- 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.
- Harry H. Kelejian & Ingmar R. Prucha & Yevgeny Yuzefovich, 2004. "Instrumental Variable Estimation Of A Spatial Autoregressive Model With Autoregressive Disturbances: Large And Small Sample Results," Advances in Econometrics, in: Spatial and Spatiotemporal Econometrics, pages 163-198, Emerald Group Publishing Limited.
- Anna Gloria Billé & Samantha Leorato, 2020. "Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 39(5), pages 437-475, May.
- Mark M. Fleming, 2004. "Techniques for Estimating Spatially Dependent Discrete Choice Models," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 7, pages 145-168, Springer.
- Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.
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- 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.
- Gilles Allaire & Eric Cahuzac & Michel Simioni, 2009. "Contractualisation et diffusion spatiale des mesures agro-environnementales herbagères," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement, INRA Department of Economics, vol. 90(1), pages 23-50.
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More about this item
Keywords
Spatial dependence; Probit model; Generalized moment estimation; Efficiency;All these keywords.
JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
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