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Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
cemmap working paper No. CWP10/21
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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. 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.
Schlagwörter: 
Nonseparable Panel
Low-Rank Approximations
Matrix Completion
Debias
Two-Way Matching
Election Day Registration
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Dokumentart: 
Working Paper

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