Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White

By Andrew J. Patton, Dimitris Nicolas Politis, Halbert White. 2009

347 citations recorded by the source corpus.

Subfields: Marketing, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/26c81081d11504c31d80fdc41a9aab5f639ed069

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Citations in the corpus

Properties of Foreign Exchange Risk Premiums (2012, methodology): “We consider an overlapping block resampling scheme which can handle serial correlation and also heteroscedasticity; see e.g. Künsch (1989), Hall et al. (1995), Politis and White (2004), Patton et al. (2009).”
The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series (2014, methodology): “The tuning parameter 1/γ is chosen by adapting the rule suggested by Politis and White (2004) (and later corrected in Patton et al. (2009)).16 Since it is for univariate data, we apply it separately to each time series and define γ as the average value.”
Probabilistic weather forecasting with machine learning (2024, positive): “We use automatic block length selection 59,60 .”
Heat-seeking sharks: support for behavioural thermoregulation in reef sharks (2012, methodology): “We resampled blocks of data randomly with replacement from the original time series and then joined them in a random order to create the uncorrelated bootstrapped sample (Carlstein et al. 1998, Politis & White 2004, Patton et al. 2009).”
A GENERALIZED BLOCK BOOTSTRAP FOR SEASONAL TIME SERIES (2014, methodology): “For concreteness, we focused on the method of Politis and White (2004), taking into account the correction discussed in Patton et al. (2009).”
Banded and tapered estimates for autocovariance matrices and the linear process bootstrap (2010, methodology): “First, we considered the block bootstrap as implemented in Canty and Ripley (2010), using the block length selection of Politis and White (2004) (see also Patton et al., 2009).”
Simultaneous multiple change-point and factor analysis for high-dimensional time series (2016, neutral): “…the finite sample distribution of the sample mean of a univariate series {Xt}Tt=1, Politis and White (2004) (accompanying corrections in Patton et al. (2009) proposed to select p as p−1 = (̂G2/̂g2(0))1/3T 1/3, where Ĝ = ∑Λ k=−Λλ(k/Λ)|k|̂R(k), ĝ(0) = ∑Λ k=−Λλ(k/Λ)̂R(k), R̂(k) = T…”
What Is the Expected Return on a Stock? (2019, neutral): “16 15 In time-series bootstraps, it is possible to implement automated procedures that determine the block length based on the properties of the time series (e.g., Politis and White (2004) and Patton, Politis, and White (2009)).”
A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing (2013, neutral): “The previous approach is clearly not the only possible multivariate extension of the procedure of Politis and White [38].”
Model averaging by jackknife criterion in models with dependent data (2013, methodology): “Although traditionally the choice of block lengths has been an issue, recent advances in automated methods (e.g., Politis and White, 2004; Patton et al., 2009) have made the selection of optimal block length practically feasible.”
GLS Estimation of Dynamic Factor Models (2011, methodology): “It is chosen optimally, based on Politis and White (2004) and including the correction of Patton, Politis, and White (2009).10 In 10 We use the MATLAB-implementation provided by Andrew Patton on his web page (http://econ.duke.edu/~ap172/code.html).”