Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241927 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP52/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrixcompletion 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-difference approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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