We explore the limits of the autoregressive (AR) sieve bootstrap, and show that its applicability extends well beyond the realm of linear time series as has been previously thought. In particular,â€¦ (More)

The paper investigates how the particular choice of residuals used in a bootstrap-based testing procedure a ects the properties of the test. The properties of the tests are investigated both underâ€¦ (More)

Statistical inference for stochastic processes with time varying spectral characteristics has received considerable attention during the last decades. We develop a nonparametric test for stationarityâ€¦ (More)

New goodness-of-fit tests for Markovian models in time series analysis are developed which are based on the difference between a fully nonparametric estimate of the one-step transition distributionâ€¦ (More)

A nonparametric bootstrap procedure is proposed for stochastic processes which follow a general autoregressive structure. The procedure generates bootstrap replicates by locally resampling theâ€¦ (More)

We propose a general bootstrap procedure to approximate the null distribution of nonparametric frequency domain tests about the spectral density matrix of a multivariate time series. Under a set ofâ€¦ (More)

A novel functional time-series methodology for short-term load forecasting is introduced. The prediction is performed by means of a weighted average of past daily load segments, the shape of which isâ€¦ (More)

Traditional kernel spectral density estimators are linear as a function of the sample autocovariance sequence. The purpose of the present paper is to propose and analyze two new spectral estimationâ€¦ (More)

A bootstrap algorithm is proposed for testing Gaussianity and linearity in stationary time series, and consistency of the relevant bootstrap approximations is proven rigorously for the first time.â€¦ (More)

We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous-pathâ€¦ (More)