Quantile cross-spectral measures of dependence between economic variables
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Date
2015-10-23
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Abstract
In this paper we introduce quantile cross-spectral analysis of multiple time series
which is designed to detect general dependence structures emerging in quantiles of
the joint distribution in the frequency domain. We argue that this type of dependence
is natural for economic time series but remains invisible when the traditional
analysis is employed. To illustrate how such dependence structures can arise between
variables in different parts of the joint distribution and across frequencies,
we consider quantile vector autoregression processes. We define new estimators
which capture the general dependence structure, provide a detailed analysis of their
asymptotic properties and discuss how to conduct inference for a general class of
possibly nonlinear processes. In an empirical illustration we examine one of the
most prominent time series in economics and shed new light on the dependence of
bivariate stock market returns.
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Keywords
cross-spectral density, copula, ranks, time series, dependence, quantiles