Get access

Chi-squared portmanteau tests for structural VARMA models with uncorrelated errors


Naoya Katayama, Faculty of Economics, Kansai University, 3-3-35 Yamate-cho, Suita, Osaka 564-8680, Japan.


We consider portmanteau tests for testing the adequacy of structural vector autoregressive moving average models with uncorrelated errors. Under the assumption that errors are uncorrelated but non-independent, it is known that the Ljung–Box (or Box–Pierce) portmanteau test statistic is asymptotically distributed as a weighted sum of chi-squared random variables which can be far from the chi-square distribution usually employed. We therefore propose a new portmanteau statistic that is asymptotically chi-squared even in the presence of uncorrelated but non-independent errors. Monte Carlo experiments illustrate the finite sample performance for the proposed portmanteau test.