Preprint
Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
ArXiv.org
Cornell University
12/19/2018
DOI: 10.48550/arxiv.1812.08089
Abstract
We study a panel data model with general heterogeneous effects where slopes are allowed to vary across both individuals and over time. The key dimension reduction assumption we employ is that the heterogeneous slopes can be expressed as having a factor structure so that the high-dimensional slope matrix is low-rank and can thus be estimated using low-rank regularized regression. We provide a simple multi-step estimation procedure for the heterogeneous effects. The procedure makes use of sample-splitting and orthogonalization to accommodate inference following the use of penalized low-rank estimation. We formally verify that the resulting estimator is asymptotically normal allowing simple construction of inferential statements for {the individual-time-specific effects and for cross-sectional averages of these effects}. We illustrate the proposed method in simulation experiments and by estimating the effect of the minimum wage on employment.
Details
- Title: Subtitle
- Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
- Creators
- Victor Chernozhukov - Massachusetts Institute of TechnologyChristian Hansen - University of ChicagoYuan LiaoYinchu Zhu
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.1812.08089
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, NY
- Language
- English
- Date posted
- 12/19/2018
- Academic Unit
- Economics
- Record Identifier
- 9984937787202771
Metrics
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