Robust Spectral Analysis

By Andreas Hagemann. 2011

56 citations recorded by the source corpus.

In this paper I introduce quantile spectral densities that summarize the cyclical behavior of time series across their whole distribution by analyzing periodicities in quantile crossings. This approach can capture systematic changes in the impact of cycles on the distribution of a time series and allows robust spectral estimation and inference in situations where the dependence structure is not accurately captured by the auto-covariance function. I study the statistical properties of quantile spectral estimators in a large class of nonlinear time series models and discuss inference both at fixed and across all frequencies. Monte Carlo experiments illustrate the advantages of quantile spectral analysis over classical methods when standard assumptions are violated.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/d315c5a42c37082a01db98b333e7e07a9b80af99

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Citations in the corpus

Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis (2011, methodology): “In contrast to the latter, however, we do not focus on a particular quantile, and consider copula cross-covariances γUk (τ1, τ2) for all (not necessarily equal) values of τ1, τ2, while Li [2008, 2011] and Hagemann [2011] restrict to the case τ1 = τ2.”
Quantile spectral analysis for locally stationary time series (2014, background): “proach. Pioneering contributions in that direction are Hong (1999) and Li (2008), who coined the names of Laplace spectrum and Laplace periodogram. The Laplace spectrum concept was further studied by Hagemann (2011), and extended into cross-spectrum and spectral kernel concepts by Dette et al. (2014”