Nonparametric Tests for Serial Independence Based on Quadratic Forms
By Cees G. H. Diks, Valentyn Panchenko. 2005
9 citations recorded by the source corpus.
Tests for serial independence and goodness-of-fit based on divergence notions between probability distributions, such as the Kullback-Leibler divergence or Hellinger distance, have recently received much interest in time series analysis. The aim of this paper is to introduce tests for serial independence using kernel-based quadratic forms. This separates the problem of consistently estimating the divergence measure from that of consistently estimating the underlying joint densities, the existence of which is no longer required. Exact level tests are obtained by implementing a Monte Carlo procedure using permutations of the original observations. The bandwidth selection problem is addressed by introducing a multiple bandwidth procedure based on a range of different bandwidth values. After numerically establishing that the tests perform well compared to existing nonparametric tests, applications to estimated time series residuals are considered. The approach is illustrated with an application to financial returns data.
Subfields: Economics and Econometrics, Finance
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
In Finance
Citations in the corpus
- A Kernel Independence Test for Random Processes (2014, positive): “Using the test of Diks & Panchenko (2005), we checked that serial dependence of the differentiated time series decays fast enough to satisfy the assumed mixing conditions (by a differentiated time series, we refer to (Xt −Xt−1)t∈N).”