• Title/Summary/Keyword: 고차원 장기억 시계열

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Controlling the false discovery rate in sparse VHAR models using knockoffs (KNOCKOFF를 이용한 성근 VHAR 모형의 FDR 제어)

  • Minsu, Park;Jaewon, Lee;Changryong, Baek
    • The Korean Journal of Applied Statistics
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    • v.35 no.6
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    • pp.685-701
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    • 2022
  • 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.