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Bootstrap-Based Statistical Inference for Dependent Data

Organiser

Participants

Bootstrap convergence rates for the maximum of an increasing number of autocovariances and autocorrelations under strict stationarity

Annika Betken (Presenter/Speaker)

A distance correlation based test for independence of time series

PROF. DR. Anna Dudek (Presenter/Speaker)

Bootstrapping nonstationary processes

Carsten Jentsch (Presenter/Speaker)

Sparsity and fusion penalization in large structural periodic vector autoregressions

Conference

65th ISI World Statistics Congress 2025 | The Hague

Category: Bernoulli Society for Mathematical Statistics and Probability (BS)

Proposal Description

When the distribution of a statistic is not accessible, statistical inference is often based on asymptotic results. However, the small sample performance of confidence sets based on asymptotic critical values can be very poor. Even more the asymptotics often depend on unknown parameters and are therefore inaccessible. The bootstrap offers a convenient way to circumvent these difficulties. This session provides an overview of recent variants the bootstrap for time series with a complex dependence structure. The topics of the talks range from theoretical results on the validity to applications e.g. in econometrics.