Predictive inference for discrete-valued time series

Abstract

For discrete-valued time series, predictive inference cannot be implemented through the construction of prediction intervals to some predetermined coverage level, as this is the case for real-valued time series. To address this problem, we propose to reverse the construction principle by considering preselected sets of interest and estimating the probability that a future observation of the process falls into these sets. The accuracy of the prediction is then evaluated by quantifying the uncertainty associated with estimation of these predictive probabilities. We consider parametric and non-parametric approaches and derive asymptotic theory for the estimators involved. Suitable bootstrap approaches to evaluate the distribution of the estimators considered also are introduced. They have the advantage to imitate the distributions of interest under different possible settings, including the practical important case where uncertainty holds true about the correctness of a parametric model used for prediction. Theoretical justification of the bootstrap is given, which also requires investigation of asymptotic properties of parameter estimators under model misspecification. We elaborate on bootstrap implementations under different scenarios and focus on parametric prediction using INAR and INARCH models and (conditional) maximum likelihood estimators. Simulations investigate the finite sample performance of the predictive method developed and applications to real life data sets are presented.