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.
Authors
![Martin Weidner](/sites/default/files/styles/square_desktop/public/2022-05/Martin_Weidner.jpg?itok=rypZqqGZ)
Research Associate University College London and University of Oxford
Martin is an IFS Research Associate, a Fellow of the Nuffield College and a Professor in the Department of Economics at the University of Oxford.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Ivan Fernandez-Val
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Hugo Freeman
Working Paper details
- DOI
- 10.47004/wp.cem.2021.1020
- Publisher
- The IFS
Suggested citation
I, Fernandez-Val and H, Freeman and M, Weidner. (2021). Low-rank approximations of nonseparable panel models. London: The IFS. Available at: https://ifs.org.uk/publications/low-rank-approximations-nonseparable-panel-models (accessed: 30 June 2024).
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