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Burn-in selection in simulating stationary time series

Author

Listed:
  • Li, Yuanbo
  • Chan, Chu Kin
  • Yau, Chun Yip
  • Ng, Wai Leong
  • Lam, Henry

Abstract

Many time series models are defined in a recursive manner, which prohibits exact simulations. In practice, one appeals to simulating a long time series and discarding a large number of initial simulated observations, known as the burn-in. For autoregressive models where the dependence decays exponentially fast, the choice of the burn-in is not critical. However, for long-memory time series where the dependence from the remote past is strong, it is not clear how to select the burn-in number. By combining several samplers with randomized burn-in numbers, a method for exactly simulating the expectation of a statistic computed from a time series is developed. Moreover, with some suitably chosen statistics, the exact simulation method can be applied to quantify the effect of burn-in numbers on the simulated sample. Simulation studies are conducted to provide some practical guidances for burn-in selections.

Suggested Citation

  • Li, Yuanbo & Chan, Chu Kin & Yau, Chun Yip & Ng, Wai Leong & Lam, Henry, 2024. "Burn-in selection in simulating stationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:csdana:v:192:y:2024:i:c:s0167947323001974
    DOI: 10.1016/j.csda.2023.107886
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