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• W4381094773 abstract "Studies on the going-on COVID-19 pandemic face small sample issues. In this context, Bayesian estimation is considered a viable alternative to frequentist estimation. Demonstrating the Bayesian approach’s advantage in dealing with this problem, our research conducted a case study concerning ASEAN economic growth during the COVID-19 pandemic. By using Monte Carlo standard errors and interval hypothesis testing to check parameter bias within a Bayesian MCMC simulation study, the author obtained significant conclusions as follows: first, in insufficient sample sizes, in contrast to frequentist estimation, the Bayesian framework can offer meaningful results, that is, expansionary monetary and contractionary fiscal policies are positively associated with economic growth; second, in the face of a small sample, by incorporating more information into prior distributions for the model parameters, Bayesian Monte Carlo simulations perform so far better than naïve Bayesian and frequentist estimation; third, in case of a correctly specified prior, the inferences are robust to different prior specifications. The author strongly recommends applying specific informative priors to Bayesian analyses, particularly in small sample investigations." @default.
• W4381094773 created "2023-06-18" @default.
• W4381094773 creator A5055175461 @default.
• W4381094773 date "2023-04-01" @default.
• W4381094773 modified "2023-10-18" @default.
• W4381094773 title "Applying Monte Carlo Simulations to a Small Data Analysis of a Case of Economic Growth in COVID-19 Times" @default.
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• W4381094773 doi "https://doi.org/10.1177/21582440231181540" @default.
• W4381094773 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37362768" @default.
• W4381094773 hasPublicationYear "2023" @default.

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