A0721
Title: Debiased inference for nonlinear panel maximum-likelihood models with two-way fixed effects
Authors: Yutao Sun - Dongbei University of Finance and Economics (China) [presenting]
Xuan Leng - Xiamen University (China)
Abstract: Panel data models often use fixed effects to account for unobserved heterogeneities. These fixed effects are typically incidental parameters, and their estimators converge slowly relative to the square root of the sample size. In the maximum likelihood context, this induces an asymptotic bias of the likelihood function. Test statistics derived from the asymptotically biased likelihood no longer follow their standard limiting distributions. This causes severe distortions in test sizes. A generic class of dynamic nonlinear models with two-way fixed effects is considered, and an analytical bias correction method for the likelihood function is proposed. It is formally shown that the likelihood ratio, the Lagrange multiplier, and the Wald test statistics derived from the corrected likelihood follow their standard asymptotic distributions. As a by-product, a bias-corrected estimator of the structural parameter can also be derived from the corrected likelihood function. The performance of the bias correction procedure is evaluated through simulations.
