Reducing Score and Information Bias in Panel Data Likelihoods

This chapter studies the role of information bias in likelihood-based estimation and inference, with a focus on its implications for small-sample performance. Drawing on Schumann et al. (2023), it shows that improvements in the finite-sample behavior of estimators and confidence regions can be traced to first-order information unbiasedness, and explains why this property is particularly important for inference rather than for point estimation. The chapter provides a unifying perspective on several well-known simulation findings in the panel data literature, including the variance-reducing effects of score bias correction, the strong coverage properties of confidence regions based on conditional likelihoods in short panels, and the superior performance of pseudolikelihood methods that are simultaneously first-order score and information unbiased.