Parameter Estimation for ARMA Models with Infinite Variance Innovations

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...

Weighted Least Absolute Deviations Estimation for Arma Models with Infinite Variance

For autoregressive and moving-average (ARMA) models with infinite variance innovations, quasi-likelihood based estimators (such as Whittle’s estimators ) suffer from complex asymptotic distributions depending on unknown tail indices. This makes the statistical inference for such models difficult. In contrast, the least absolute deviations estimators (LADE) are more appealing in dealing with hea...

Multichannel ARMA Parameter Estimation

Using a perturbation matrix, we introduced a hyperplane used to define a generalized null-spectrum, based on both the signal and noise subspaces, while the MUSIC and Min-Norm null-spectra are defined based only on the noise subspace. With the generalized nullspectrum, we derived the upper and lower bounds of a class of the generalized null-spectrum, called the maximum and minimum null-spectra, ...

Online Appendix for Portmanteau Tests for ARMA Models with Infinite Variance

On Parameter Estimation for Exponential Dispersion Arma Models

A class of autoregressive moving-average (ARMA) models proposed by J rgensen and Song [Journal of Applied Probability (1998), vol. 35, pp. 78–92] with exponential dispersion model margins are useful to deal with non-normal stationary time series with high-order autocorrelation. One property associated with the class of models is that the projection process takes the exact form of the classical ...