2018/06/13 by Jamie Halliday, Halliday, Jamie, Georgi N. Boshnakov +1
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.1806.04892
openalex publication_date 2018/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
This paper introduces multivariate Poisson autoregressive models with exogenous covariates (PoARX) for modelling multivariate time series of counts. We obtain conditions for the PoARX process to be stationary and ergodic before proposing a computationally efficient procedure for estimation of parameters by the method of inference functions (IFM) and obtaining asymptotic normality of these estimators. Lastly, we demonstrate an application to count data for the number of people entering and exiting a building, and show how the different aspects of the model combine to produce a strong predictive model. We conclude by suggesting some further areas of application and by listing directions for future work.