On the choice of parameters for the local block bootstrap in the local stationary setting

Music City Center 

Dealing with time-varying linear processes, their stationary companion processes come in handy for proving various results. However, espacially considering limit distributions, their lack of observability hamper statistical procedures like hypothesis testing. In this case, the so-called local block bootstrap established by Dowla et al. (2013) provides a sound way out. Said bootstrap procedure is based on the choice of different bootstrap parameters which each have a distinct impact on the simulation results. We illustrate the influence of different parameter choices with an extended simulation study using alpha-stable distributions in combination with empirical characteristic functions. The former is a wide class of distributions ensuring the transferability of our results, whereas the latter opens the way to various procedures including independence testing. Additionally, we present a bootstrap central limit theorem allowing for the formulation of bootstrap confidence intervals by the pivotal method without relying on the normal distribution.

Local stationarity

Local block bootstrap

Central limit theorem

Nonparametric statistics 

Section on Nonparametric Statistics