Robust Estimation of Integer-ValuedTime Series Models / Ahmed Ali Muhammad ؛ Mohamed Ali Ismail

Thesis (M.Cs.)-Cairo nivsersity,2022.

Bibliography: p. 93-100.

This work extends a robust estimation method for first order integer-valued autoregressive models with Poisson innovations to integer-valued autoregressive moving average models of arbitrary order. It uses a Monte Carlo simulation to investigate the performance of the extensions relative to the traditional estimation methods of Yule-Walker, conditional least squares and conditional maximum likelihood under a variety of design conditions. Overall, the work concludes that the extensions provide significant improvement in performance if the data is contaminated with additive outliers. If the data is contaminated with innovation outliers, conditional least squares appears to be more suitable for estimation of the autoregressive and moving average coefficients while the extensions perform better for the estimation of other parameters. However, the improvement in performance might not be enough for some applications. In such cases, we suggest that the extensions be used as part of more intricate estimation procedures.