Low-rank approximations of nonseparable panel models

Ivan Fernandez-Val (), Hugo Freeman and Martin Weidner ()
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Hugo Freeman: Institute for Fiscal Studies
Martin Weidner: Institute for Fiscal Studies and University College London

No CWP10/21, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: 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.

Date: 2021-03-04
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Low-rank approximations of nonseparable panel models (2021)
Working Paper: Low-rank approximations of nonseparable panel models (2020)
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