Bootstrap Inference in Autoregressive Duration Models
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摘要
This paper develops fixed-count bootstrap methods for likelihood-based inference in autoregressive conditional duration (ACD) models with random calendar-time spans, addressing finite-mean, boundary, and infinite-mean regimes and the resulting non-standard limit behavior. It shows fixed-count bootstrap is valid for κ ≥ 1 and yields a Gaussian bootstrap t statistic for all κ > 0, while percentile bootstrap remains first-order valid under 0 < κ < 1. The results are supported by Monte Carlo simulations and an empirical application to cryptocurrency ETFs. [page::1]
