A MPEC Estimator for Misclassification Models

In this paper, we propose a constrained maximum likelihood estimator for misclassification models, by formulating the estimation as an MPEC (Mathematical Programming with Equilibrium Constraints) problem. Our approach improves the numerical accuracy and avoids the singularity problem. Monte Carlo simulations confirm that the proposed estimator reduces bias and standard deviation of the estimator, especially when the sample is small/medium and/or the dimension of matrix is large.

Keywords: Mathematical Programming with Equilibrium Constraints (MPEC); Misclassification models; Nonparametric estimation

Suggested Citation: Suggested Citation

Lu, Ruichang and Luo, Yao and Xiao, Ruli, A MPEC Estimator for Misclassification Models (July 3, 2014). Economics Letters, Volume 125, 2014, Available at SSRN: https://ssrn.com/abstract=2352935 or http://dx.doi.org/10.2139/ssrn.2352935