Integrated Likelihood-Based Inference for Nonlinear Panel Data Models
摘要
This chapter presents an integrated likelihood approach to the estimation of nonlinear panel data models with individual-specific fixed-effects. Building on the integrated likelihood framework of Severini (2007), the proposed method yields a likelihood that more closely approximates a genuine parametric likelihood than existing approaches in the literature. The statistical properties of the estimator are developed within an asymptotic framework in which both the cross-sectional and time dimensions grow without bound.