Bootstrap-After-Bootstrap Prediction Intervals for Autoregressive Models

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Empirical prediction intervals revisited (2014, methodology): “Bootstrap approaches (e.g. Stine 1985; Thombs & Schucany 1990; Kim 2001; Reeves 2005) have been used to address this problem of parameter uncertainty.”
Bootstrapping Financial Time Series (2002, methodology): “However, if the presence of conditional heteroscedasticity is not taken into account, the coverage properties of bootstrap intervals for high frequency returns can be distorted; see, for example, Kim (2001) in the context of VAR(1) models.”
Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals (2011, methodology): “Following Kilian (1998), Clements and Taylor (2001) and Kim (2001, 2004) propose the use of the bias-corrected bootstrap for the AR model, in which bias-correction is conducted in two stages of the bootstrap.”
A WARP-SPEED METHOD FOR CONDUCTING MONTE CARLO EXPERIMENTS INVOLVING BOOTSTRAP ESTIMATORS (2013, unknown): “Jn(x;P� n)! � J(x;P) as n ! 1: ( 11 ) Corollary 1. Suppose Assumptions 3.1-3.7 hold.”