Portmanteau tests for ARMA models with infinite variance

Portmanteau Tests for Arma Models with Infinite Variance

Autoregressive and moving-average (ARMA) models with stable Paretian errors is one of the most studied models for time series with infinite variance. Estimation methods for these models have been studied by many researchers but the problem of diagnostic checking fitted models has not been addressed. In this paper, we develop portmanteau tests for checking randomness of a time series with infini...

Online Appendix for Portmanteau Tests for ARMA Models with Infinite Variance

Corrected portmanteau tests for VAR models with time-varying variance

The problem of test of fit for Vector AutoRegressive (VAR) processes with unconditionally heteroscedastic errors is studied. The volatility structure is deterministic but time-varying and allows for changes that are commonly observed in economic or financial multivariate series such as breaks or smooth transitions. Our analysis is based on the residual autocovariances and autocorrelations obtai...

Kernel-based portmanteau diagnostic test for ARMA time series models

In this paper, the definition of the Toeplitz autocorrelation matrix is used to derive a kernel-based portmanteau test statistic for ARMA models. Under the null hypothesis of no serial correlation, the distribution of the test statistic is approximated by a standard normal using the kernel-based normalized spectral density estimator, without having to specify any alternative model. Unlike most ...

Mixed Portmanteau Tests for Time-series Models

This paper obtains the joint limiting distribution of residuals and squared residuals of a general time-series model. Based on this, we propose a mixed portmanteau statistic for testing the adequacy of fitted time-series models. In some cases, it is shown that this statistic can be simply approximated by the sum of well-known portmanteau statistics. The finite-sample performance of the new test...