A0429
Title: GCov-based Portmanteau test
Authors: Joann Jasiak - York University (Canada)
Aryan Manafi Neyazi - York University (Canada) [presenting]
Abstract: The purpose is to study nonlinear serial dependence tests for non-Gaussian time series and residuals of dynamic models based on Portmanteau statistics involving nonlinear autocovariances. A new NLSD test with an asymptotic chi-square distribution is introduced to test nonlinear serial dependence in time series. This test is inspired by the generalized covariance (GCov) residual-based specification test, recently proposed as a diagnostic tool for semi-parametric dynamic models with i.i.d. non-Gaussian errors. It has a chi-square distribution when the model is correctly specified and estimated by the GCov estimator. It is extended by introducing a GCov bootstrap test for residual diagnostics when the model is estimated by a different method, such as the maximum likelihood estimator under a parametric assumption on the error distribution. The GCov specification test is reviewed, and new asymptotic results are derived under local alternatives for testing hypotheses on the parameters of a semi-parametric model. A simulation study shows that the tests perform well in applications to mixed causal-noncausal univariate and multivariate autoregressive models. The GCov specification test is used to assess the fit of a mixed causal-noncausal model of aluminum prices with locally explosive patterns, i.e. bubbles and spikes between 1990 and 2023.
