Low-rank approximations of nonseparable panel models
Fernandez-Val, Ivan; Freeman, Hugo; Weidner, Martin
We provide estimation methods for panel nonseparable 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-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.
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