Low-rank approximations of nonseparable panel models

Hugo Freeman and Martin Weidner
Authors registered in the RePEc Author Service: Ivan Fernandez-Val

The Econometrics Journal, 2021, vol. 24, issue 2, C40-C77

Abstract: SummaryWe 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 US illustrate the properties and usefulness of our methods.

Keywords: Nonseparable panel; low-rank approximations; matrix completion; debias; two-way matching; election day registration (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Low-rank approximations of nonseparable panel models (2021)
Working Paper: Low-rank approximations of nonseparable panel models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:24:y:2021:i:2:p:c40-c77.

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