Modeling air quality level with a flexible categorical autoregression

To study urban air quality, this paper proposes a novel categorical time series model, which is based on a linear combination of bounded Poisson distribution and discrete distribution to describe the dynamic and systemic features of air quality, respectively. Daily air quality level data of three major cities in China, including Beijing, Shanghai and Guangzhou, are analyzed. It is concluded that the air quality in Beijing is the worst among the three cities but is gradually improving, and its dynamics is also the most pronounced. Theoretically, the design of our model increases the flexibility of the probabilistic structure while ensuring a dynamic feedback mechanism without high computational stress. We estimate the parameters through an adaptive Bayesian Markov chain Monte Carlo sampling scheme and show the satisfactory finite sample performance of the model through simulation studies.

Automated summary

This paper proposes a novel categorical time series model to study urban air quality, and analyzes data from three major cities in China. The results indicate that Beijing has the worst air quality but it is gradually improving, and the proposed model shows satisfactory performance through simulation studies using an adaptive Bayesian Markov chain Monte Carlo sampling scheme.