The Block-Block Bootstrap: Improved Asymptotic Refinements

By Donald W. K. Andrews. 2002

74 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/027252969158a20a9a006efefa45a34366e11ba2

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Citations in the corpus

Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes (2008, unknown): “…of the block bootstrap has led to the search for other ways to implement the bootstrap with dependent data and to the development of adaptations designed to increase the asymptotic refinement of the block bootstrap (see, e.g. Horowitz, 2003; Andrews, 2004, and the references contained therein).”
Inference on Causal and Structural Parameters using Many Moment Inequalities (2013, methodology): “The BMB method can be considered as a variant of the tapered block bootstrap (see Paparoditis and Politis, 2001, 2002; Andrews, 2004) applied to non-overlapping blocks with a rectangular tapering function.”
Bootstrap Methods in Econometrics (2018, unknown): “Andrews (2004), Härdle, Horowitz, and Kreiss (2003); Hall and Horowitz (1996); and Hall, Horowitz, and Jing (1995), among other references cited in this section, provide details and numerical illustrations.”
Testing Many Moment Inequalities (2013, methodology): “The BMB method can be considered as a variant of the tapered block bootstrap (see Paparoditis and Politis, 2001, 2002; Andrews, 2004) applied to non-overlapping blocks with a rectangular tapering function.”
Bootstrap Methods for Time Series (2003, methodology): “Andrews (2002b) presents Monte Carlo evidence indicating that the block-block bootstrap also has improved finite-sample performance.”
Bootstrap Hypothesis Testing (2007, methodology): “Andrews (2004) proposes an ingenious block-block bootstrap that involves modifying the original test statistic, so that its join-point features resemble those of the bootstrap statistics.”
BOOTSTRAPPING REALIZED VOLATILITY (2009, methodology): “We conjecture that one way of inducing the third-order accuracy of the i.i.d. bootstrap under constant volatility is to transform the original statistic Th so as to more closely match the behavior of T ∗h (see Andrews (2004) for a similar approach).”
Bootstrap Methods in Econometrics (2006, unknown): “However, the rate of improvement is generally quite small; see Hall et al. (1995) and Andrews (2002, 2004).”