A small sigma approach to certain problems in errors-in-variables models

Abstract

We propose a pragmatic approach to the errors-in-variables and nonlinear panel models. These models are often deemed impossible to estimate in their most general forms. For example, the higher order moments approach to errors-in-variables model fails when there is conditional heteroscedasticity. We propose estimating these models using approximate moments, using a Taylor series approximation applied to Kadane’s (1971) small sigma approach.