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Title: Bootstrap-based inference for sparse high-dimensional time series models Authors:  Jonas Krampe - University of Mannheim (Germany) [presenting]
Jens-Peter Kreiss - Technische Universitaet Braunschweig (Germany)
Efstathios Paparoditis - University of Cyprus (Cyprus)
Abstract: Fitting sparse models to high dimensional time series is an important area of statistical inference. We consider sparse vector autoregressive models and develop appropriate bootstrap methods to infer properties of such processes, like the construction of confidence intervals and of tests for individual or for groups of model parameters. The bootstrap methodology generates pseudo time series using a model-based bootstrap procedure which involves an estimated, sparsified version of the underlying vector autoregressive model. Inference is performed using so-called de-sparsified or de-biased estimators of the autoregressive model parameters. We derive the asymptotic distribution of such estimators in the time series context and establish asymptotic validity of the bootstrap procedure proposed for estimation and, appropriately modified, for testing purposes. In particular, we focus on testing that a group of autoregressive coefficients equals zero. The theoretical results are complemented by simulations which investigate the finite sample performance of the bootstrap methodology proposed. A real-life data application is also presented.