Dimitris Nicolas Politis

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The situation where the available data arise from a general linear process with a unit root is discussed. We propose a modiication of the Block Bootstrap which generates replicates of the original data and which correctly imitates the unit root behavior and the weak dependence structure of the observed series. Validity of the proposed method for estimating(More)
A new time series bootstrap scheme, the Time Frequency Toggle (TFT)-Bootstrap, is proposed. Its basic idea is to bootstrap the Fourier coefficients of the observed time series, and then back-transforming them to obtain a bootstrap sample in the time domain. Related previous proposals, such as the 'surrogate data' approach, re-sampled only the phase of the(More)
10 11 The Unobserved ARCH model is a good description of the phenomenon of changing volatility that is commonly appeared in the financial time series. We study this model adopting Bayesian inference via Markov Chain Monte Carlo (MCMC). In order to provide an easy to implement MCMC algorithm we adopt some suitable non-linear transformations of the parameter(More)
A well known result of Burg (1967) and Kunsch (1981) identifies a Gaussian Markov random field with autocovariances specified on a finite part L of the n-dimensional integer lattice, as the random field with maximum entropy among all random fields with same autocovariance values on L. A simple information theoretic proof of a version of the maximum entropy(More)
In this paper we show that the linear process bootstrap (LPB) and the autore-gressive sieve bootstrap (AR sieve) fail in general for statistics whose large-sample distribution depends on higher order features of the dependence structure rather than just on autocovari-ances. We discuss why this is still the case under linearity if it does not come along with(More)