Post-Selection Inference in Three-Dimensional Panel Data
Harold Chiang,
Joel Rodrigue and
Yuya Sasaki
Papers from arXiv.org
Abstract:
Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimonious model and the true model is rich, then it incurs mis-specification biases. When one employs a rich model and the true model is parsimonious, then it incurs larger standard errors than necessary. It is therefore useful for researchers to know correct models. In this light, Lu, Miao, and Su (2018) propose methods of model selection. We advance this literature by proposing a method of post-selection inference for regression parameters. Despite our use of the lasso technique as means of model selection, our assumptions allow for many and even all fixed effects to be nonzero. Simulation studies demonstrate that the proposed method is more precise than under-fitting fixed effect estimators, is more efficient than over-fitting fixed effect estimators, and allows for as accurate inference as the oracle estimator.
Date: 2019-03, Revised 2019-04
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Citations: View citations in EconPapers (2)
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http://arxiv.org/pdf/1904.00211 Latest version (application/pdf)
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Journal Article: POST-SELECTION INFERENCE IN THREE-DIMENSIONAL PANEL DATA (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1904.00211
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