Abstract
In this paper, it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. The algorithm is applied to forecasts from three non-nested diffusion models for S shaped processes like virus diffusion. An illustration to daily data on first-wave cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the newly combined forecasts.
Highlights
Virus diffusion often obeys an S shaped pattern
In this paper it is proposed to assign weights based on insample performance of each of the models, as reflected by their posterior probabilities, see Leamer (1978) for the first discussion of Bayesian model averaging
Bayesian methods may sometimes be difficult to handle, but here I rely on an important result in Raftery (1995, page 145), which is that the posterior probability can be expressed as a simple function of the values of the Bayesian Information Criterion (BIC) (Schwarz 1978)
Summary
Virus diffusion often obeys an S shaped pattern. Consider for example the daily cumulative new cases of Covid-19 in the Netherlands in Figure 1, for the sample February 27, 2020 with the first case, and May 19, 2020 with 44249 cases. Bayesian methods may sometimes be difficult to handle, but here I rely on an important result in Raftery (1995, page 145), which is that the posterior probability can be expressed as a simple function of the values of the Bayesian Information Criterion (BIC) (Schwarz 1978). Such a BIC value is with the estimated maximum likelihood and many statistical packages report BIC values.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
