Harvey, A. C. and Kattuman, P.

Time Series Models Based on Growth Curves with Applications to Forecasting Coronavirus

Harvard Data Science Review

: Special Issue 1 - COVID-19

Abstract: Time series models are developed for predicting future values of a variable that when cumulated is subject to an unknown saturation level. Such models are relevant for many disciplines, but here attention is focused on the spread of epidemics and the applications are for coronavirus. The time series models are relatively simple but are such that their specification can be assessed by standard statistical test procedures. In the generalized logistic class of models, the logarithm of the growth rate of the cumulative series depends on a time trend. Allowing this trend to be time-varying introduces further flexibility and enables the effects of changes in policy to be tracked and evaluated.

Keywords: generalized logistic, Gompertz curve, Kalman filter, negative binomial distribution, score-driven models, stochastic trend

Author links: Andrew Harvey  

Publisher's Link: https://hdsr.mitpress.mit.edu/pub/ozgjx0yn

COVID-19 Economic Research Special Feature: Time Series Models Based on Growth Curves with Applications to Forecasting Coronavirus