The socio-economic determinants of the coronavirus disease (COVID-19) pandemic

Viktor Stojkoski; Zoran Utkovski; Petar Jolakoski; Dragan Tevdovski; Ljupco Kocarev

Posterior inclusion probabilities offer a more robust way of determining the effect of a variable in a model, as opposed to using p-values for determining statistical significance of a model coefficient because they incorporate the uncertainty of model selection. According to equations (S3) and (S4), it is clear that we need to specify priors for the parameters of each model and for the model probability itself. To keep the model simple and easily implemented here we use the most often implemented priors. In other words, for the parameter space we elicit a prior on the error variance that is proportional to its inverse, p(σ 2 ) ≈ 1/σ 2 , and a uniform distribution on the intercept, p(α) → 1, while the Zellner's g-prior is used for the β m parameters, and for the model space we utilise the Beta-Binomial prior. To estimate the posterior parameters we use a Markov Chain Monte Carlo (MCMC) sampler, and report results from a run with 200 million recorded drawings and after a burn-in of 100 million discarded drawings. The theoretical background behind our setup can be read in Refs. [18, [79] [80] [81] .