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EM algorithms for ordered probit models with endogenous regressors

Author

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  • Hiroyuki Kawakatsu
  • Ann G. Largey

Abstract

We propose an EM algorithm to estimate ordered probit models with endogenous regressors. The proposed algorithm has a number of computational advantages in comparison to direct numerical maximization of the (limited information) log-likelihood function. First, the sequence of conditional M(aximization)-steps can all be computed analytically. Second, the algorithm updates the model parameters so that positive definiteness of the covariance matrix and monotonicity of cutpoints are naturally satisfied. Third, the variance parameters normalized for identification can be activated to accelerate convergence of the algorithm. The algorithm can be applied to models with dummy endogenous, continuous endogenous or latent endogenous regressors. A small Monte Carlo simulation experiment examines the finite sample performance of the proposed algorithms. Copyright The Author(s). Journal compilation Royal Economic Society 2009

Suggested Citation

  • Hiroyuki Kawakatsu & Ann G. Largey, 2009. "EM algorithms for ordered probit models with endogenous regressors," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 164-186, March.
    • Handle: RePEc:ect:emjrnl:v:12:y:2009:i:1:p:164-186
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    1. Seya, Hajime & Nakamichi, Kumiko & Yamagata, Yoshiki, 2016. "The residential parking rent price elasticity of car ownership in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 123-134.
    2. Giulia Bettin & Riccardo Lucchetti, 2009. "Instrumental Variable Interval Regression," EHUCHAPS, in: Ignacio Díaz-Emparanza & Petr Mariel & María Victoria Esteban (ed.), Econometrics with gretl. Proceedings of the gretl Conference 2009, edition 1, chapter 6, pages 91-107, Universidad del País Vasco - Facultad de Ciencias Económicas y Empresariales.
    3. Haucap, Justus & Heimeshoff, Ulrich, 2014. "The happiness of economists: Estimating the causal effect of studying economics on subjective well-being," International Review of Economics Education, Elsevier, vol. 17(C), pages 85-97.
    4. Zhu, Zhongkun & Ma, Wanglin & Sousa-Poza, Alfonso & Leng, Chenxin, 2020. "The effect of internet usage on perceptions of social fairness: Evidence from rural China," China Economic Review, Elsevier, vol. 62(C).
    5. Pavitra Paul & Ulrich Nguemdjo & Natalia Kovtun & Bruno Ventelou, 2021. "Does Self-Assessed Health Reflect the True Health State?," IJERPH, MDPI, vol. 18(21), pages 1-16, October.
    6. Hongyun Zheng & Wanglin Ma & Junpeng Li & Julio Botero, 2023. "Relationship between Internet Use and Negative Affect," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 18(4), pages 1693-1713, August.
    7. Kajal Lahiri & Liu Yang, 2021. "Estimating Endogenous Ordered Response Panel Data Models with an Application to Income Gradient in Child Health," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 207-243, November.
    8. Vargas, Jose P Mauricio, 2012. "Binding Constraints: Does Firm Size Matter?," MPRA Paper 41286, University Library of Munich, Germany.
    9. Mr. Mauricio Vargas, 2015. "Identifying Binding Constraints to Growth: Does Firm Size Matter?," IMF Working Papers 2015/003, International Monetary Fund.
    10. Rainer Hirk & Kurt Hornik & Laura Vana, 2019. "Multivariate ordinal regression models: an analysis of corporate credit ratings," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 507-539, September.
    11. Giulia Bettin & Riccardo Lucchetti, 2012. "Interval regression models with endogenous explanatory variables," Empirical Economics, Springer, vol. 43(2), pages 475-498, October.

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