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http://dx.doi.org/10.5351/KJAS.2022.35.6.685

Controlling the false discovery rate in sparse VHAR models using knockoffs  

Minsu, Park (Department of Statistics, Sungkyunkwan University)
Jaewon, Lee (Department of Statistics, Sungkyunkwan University)
Changryong, Baek (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.6, 2022 , pp. 685-701 More about this Journal
Abstract
FDR is widely used in high-dimensional data inference since it provides more liberal criterion contrary to FWER which is known to be very conservative by controlling Type-1 errors. This paper proposes a sparse VHAR model estimation method controlling FDR by adapting the knockoff introduced by Barber and Candès (2015). We also compare knockoff with conventional method using adaptive Lasso (AL) through extensive simulation study. We observe that AL shows sparsistency and decent forecasting performance, however, AL is not satisfactory in controlling FDR. To be more specific, AL tends to estimate zero coefficients as non-zero coefficients. On the other hand, knockoff controls FDR sufficiently well under desired level, but it finds too sparse model when the sample size is small. However, the knockoff is dramatically improved as sample size increases and the model is getting sparser.
Keywords
knockoff; high-dimensional long memory time series; FDR;
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