[Statlist] Friday, April 7, 2017 with Tommaso Proietti, University of Rome, Tor Vergata

[Maurer Letizia]

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ETH and University of Zurich

Organisers:

Proff. P. Bühlmann - L. Held - T. Hothorn - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf
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We are glad to announce the following talk:

Friday, April 7, 2017 at 15.15h  ETH Zurich HG G 19.1
with Tommaso  Proietti, University of Rome, Tor Vergara
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Title:

Optimal linear prediction of stochastic trends<https://www.math.ethz.ch/sfs/news-and-events/research-seminar.html?s=fs17#e_9805><https://www.math.ethz.ch/sfs/news-and-events/research-seminar.html?s=fs17#e_9805>


Abstract:

A recent strand of the time series literature has considered the problem of estimating high-dimensional autocovariance matrices, for the purpose of out of sample prediction. For an integrated time series, the Beveridge-Nelson trend is defined as the current value of the series plus the sum of all forecastable future changes. For the optimal linear projection of all future changes into the space spanned by the past of the series, we need to solve a high-dimensional Toeplitz system involving 𝑛 autocovariances, where 𝑛 is the sample size. The paper proposes a non-parametric estimator of the trend that relies on banding, or tapering, the sample partial autocorrelations, by a regularized Durbin-Levinson algorithm. We derive the properties of the estimator and compare it with alternative parametric estimators based on the direct and indirect finite order autoregressive predictors.


This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar

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