Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes

Understand

We study a panel data model with general heterogeneous effects where slopes are allowed to vary across both individuals and over time.

• The key dimension reduction assumption we employ is that the heterogeneous slopes can be expressed as having a factor structure so that the high-dimensional slope matrix is low-rank and can thus be estimated using low-rank regularized regression.
• We provide a simple multi-step estimation procedure for the heterogeneous effects.
• The procedure makes use of sample-splitting and orthogonalization to accommodate inference following the use of penalized low-rank estimation.