Simulation-Based Estimation of the Structural Errors-in-Variables Negative Binomial Regression Model with an Application |
Jie Q. Guo |
and Tong Li |
This paper studies the effects and estimation of
errors-in-variables negative binomial regression model. We prove that in the presence of measurement errors, in general, maximum
likelihood estimator of the overdispersion using the observed data is biased upward. We adopt a structural approach assuming
that the distribution of the latent variables is known and propose a simulation-based corrected maximum likelihood estimator
and a simulation-based corrected score estimator to estimate the |
Key
Words:
Count Data; Measurement Errors; Overdispersion; Simulation-based Corrected Score Estimator; Health Care Demand. |
JEL Classification Numbers: C13, C15, C51. |