A Neyman-Orthogonalization Approach to the Incidental Parameter Problem in Likelihood Models

Understand

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero.

• Such first-order orthogonalization allows the estimator of the nuisance parameters to converge at a slower-than-parametric rate.
• It may, however, not suffice when the nuisance parameters are very imprecisely estimated.
• Leading examples are models for panel and network data that feature fixed effects.