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dc.contributor.authorFernandez-Val, Ivanen_US
dc.contributor.authorFreeman, Hugoen_US
dc.contributor.authorWeidner, Martinen_US
dc.date.accessioned2021-03-23T15:14:26Z
dc.date.available2021-03-23T15:14:26Z
dc.date.issued2020
dc.identifier.citationIvan Fernandez-Val, Hugo Freeman, Martin Weidner. "Low-Rank Approximations of Nonseparable Panel Models." https://arxiv.org/abs/2010.12439.
dc.identifier.urihttps://hdl.handle.net/2144/42308
dc.description.abstractWe 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.en_US
dc.description.urihttps://arxiv.org/abs/2010.12439
dc.language.isoen_US
dc.titleLow-rank approximations of nonseparable panel modelsen_US
dc.typeArticleen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Economicsen_US
pubs.publication-statusSubmitteden_US
dc.description.oaversionFirst author draft
dc.identifier.mycv590626


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