Nonparametric Tests for Serial Independence Based on Quadratic Forms

Arbeitspapier

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 approac! h is illustrated with an application to financial returns data.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. 05-076/1

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Statistical Simulation Methods: General
• Subject: Bandwidth selection ; Nonparametric tests ; Serial independence ; Quadratic forms ; Nichtparametrisches Verfahren ; Statistischer Test ; Monte-Carlo-Methode ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Diks, Cees ; Panchenko, Valentyn
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2005

Further information

• Handle: http://hdl.handle.net/10419/86468
• Last update: 10.03.2025, 11:43 AM CET