Zero-modified Count Time Series with Markovian Intensities

TL;DR

This paper introduces a novel approach for modeling count time series with zero inflation or deflation using zero-modified Poisson and negative binomial models driven by Markovian intensities, estimated via generalized Kalman filtering.

Contribution

It develops a new zero-modified count time series model with Markovian intensities and provides an estimation framework using generalized Kalman filter, applicable to real and simulated data.

Findings

Effective modeling of zero-inflated count data.

Successful application to real-world datasets.

Robust estimation method demonstrated.

Abstract

This paper proposes a method for analyzing count time series with inflation or deflation of zeros. In particular, zero-modified Poisson and zero-modified negative binomial series with intensities generated by non-negative Markov sequences are studied in detail. Parameters of the model are estimated by the method of estimating equations which is facilitated by expressing the model in a generalized state space form. The latent intensities required for estimation are extracted using generalized Kalman filter. The applications of proposed model and its estimation methods are illustrated using simulated and real data sets.