It is well known that time series of returns are characterized by volatility clustering and
excess kurtosis. Therefore, when modelling the dynamic behavior of returns, inference and …

In order to construct prediction intervals without the cumbersome–and typically unjustifiable–
assumption of Gaussianity, some form of resampling is necessary. The regression set-up …

In this study, we propose a new bootstrap strategy to obtain prediction intervals for
autoregressive integrated moving‐average processes. Its main advantage over other …

In this paper we propose bootstrap methods for constructing nonparametric prediction
intervals for a general class of linear processes. Our approach uses the AR (∞)-sieve …

A new clustering method for time series is proposed, based on the full probability density of
the forecasts. First, a resampling method combined with a nonparametric kernel estimator …

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A new method to construct nonparametric prediction intervals for nonlinear time series data
is proposed. Within the framework of the recently developed sieve bootstrap, the new …

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of
Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality …

The problem of clustering time series is studied for a general class of non-parametric
autoregressive models. The dissimilarity between two time series is based on comparing …

We propose formal and quantitative measures of the risk that future inflation will be
excessively high or low relative to the range preferred by a private sector agent. Unlike …