Inference for Large-Dimensional Panel Data with Many Covariates(Paper)(Code)(Slides) (I) Statistical learning of large panel data Co-author: Markus Pelger
We propose Panel Multiple Testing that allows us to select covariates that explain a large cross-section with false discovery control. In our empirical asset pricing study, we select sparse risk factors from a factor zoo of 114, to explain 243 doubly-sorted portfolio excess returns.
NASMES 2023, AMES 2023, INFORMS 2023, 11th Western Conference on Mathematical Finance, NBER-NSF SBIES 2022, California Econometrics Conference 2022, Stanford HAI Financial Services Industry Review
Large Dimensional Change Point Detection with FWER Control as Automatic Stopping(Paper)(Poster)(Code) (I) Statistical learning of large panel data Co-authors: Yang Fan, Markus Pelger
With hundreds of time series and unknown number of change points to detect, our inference-based method is better suited than the classical DP-based algorithm due to its conscientious trade-off of Type I vs Type II error. We provide FWER control theory. In simulations, we showed 20% lift in F1 scores against leading benchmarks.
