Publication: A joint portmanteau test for conditional mean and variance time-series models
Loading...
Identifiers
Publication date
2015-01-01
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Impact
Export
Abstract
In this article, we propose a new joint portmanteau test for checking the specification of parametric conditional mean and variance functions of linear and nonlinear time-series models. The use of a joint test is motivated for complete control of the asymptotic size since marginal tests for the conditional variance may lead to misleading conclusions when the conditional mean is misspecified. The new test is based on an asymptotically distribution-free transformation on the sample autocorrelations of both normalized residuals and squared normalized residuals. This makes it unnecessary to full detail the asymptotic properties of the estimates used to obtain residuals, which could be inefficient two-step ones, avoiding also choices of maximum lag parameters increasing with sample length to control asymptotic size. The robust versions of the new test also properly account for higher-order moment dependence at a reduced cost. The finitesample performance of the new test is compared with that of well-known tests through simulations.
Description
Keywords
Model diagnostic checking, Portmanteau statistic, Estimation effect, GARCH model specification testing, Residual serial correlation
Bibliographic citation
Velasco, C. and X. Wang. A joint portmanteau test for conditional mean and variance time series models. Journal of Time Series Analysis, 2015, 36, 39-60