Low-Rank Approximations of Nonseparable Panel Models

Understand

We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations.

• The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data.
• We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases.
• We correct these biases using matching and difference-in-differences approaches.