Testing for Serial Independence: Beyond the Portmanteau Approach

ABSTRACT

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.

KEYWORDS:

• Chi-squared statistic
• Model diagnostic checking
• Multi-way contingency table
• Nonlinear time series
• Portmanteau approach
• Serial dependence

Supplementary Materials

The online supplementary material contains the appendices for the article.

Additional information

Funding

Antonio Punzo gratefully acknowledges the financial support from the grant “Finite mixture and latent variable models for causal inference and analysis of socio-economic data” (FIRB 2012-Futuro in Ricerca) funded by the Italian Government (RBFR12SHVV).