High-Dimensional Knockoffs Inference for Time Series Data

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Abstract
We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. We also generalize the robust knockoffs inference in [4] to the time series setting to relax the assumption of known covariate distribution required by model-X knockoffs, since such an assumption is overly stringent for time series data. We establish sufficient conditions under which TSKI achieves the asymptotic false discovery rate (FDR) control. Our technical analysis reveals the effects of serial dependence and unknown covariate distribution on the FDR control. We conduct a power analysis of TSKI using the Lasso coefficient difference knockoff statistic under the generalized linear time series models. The finite-sample performance of TSKI is illustrated with several simulation examples and an economic inflation study.
Funding Information
  • NSF grants (DMS-1953356)
  • NSF grants (EF-2125142)
  • NSF grants (DMS-2310981)
  • NSF grants (DMS-2324490)
  • NSF grants (111-2118-M-001-012-MY2)
  • NSF grants (109-2118-M-007-MY3)
  • National Science and Technology Council, Taiwan

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