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Title: Extending the range of validity of autoregressive bootstrap methods Authors:  Efstathios Paparoditis - University of Cyprus (Cyprus) [presenting]
Abstract: Two modifications of the autoregressive-sieve respectively autoregressive bootstrap are proposed. The first modification replaces the classical i.i.d. resampling scheme applied to the residuals of the autoregressive fit by a generation of i.i.d. wild pseudo-innovations that appropriately mimic the first, the second and the fourth order moment structure of the true innovations driving the underlying linear process. This modification extends the validity of the autoregressive-sieve bootstrap to classes of statistics for which the classical, residual-based autoregressive-sieve bootstrap fails. In the second modification, an autoregressive bootstrap applied to an appropriately transformed time series is proposed which, together with a dependent-wild type generation of pseudo-innovations, delivers a bootstrap procedure which is valid for large classes of statistics and for stochastic processes that satisfy quite general weak dependent conditions. A fully data-driven selection of the bootstrap parameters involved in both modifications is proposed, while extensive simulations, including comparisons with alternative bootstrap methods, show a good finite sample performance of the proposed bootstrap procedures.