全球智库信息集成服务系统(G2TT): DP15951 Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP15951
	DP15951 Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs
	Jonas Arias; Jesus Fernandez-Villaverde; Juan Francisco Rubio-Ramírez; Minchul Shin
发表日期	2021-03-22
出版年	2021
语种	英语
摘要	We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model captures the data dynamics very well, including the three waves of infections. We use the estimated (true) number of new cases and the time-varying effective reproduction number from the epidemiological model as information for structural vector autoregressions and local projections. We document how additional government-mandated mobility curtailments would have reduced deaths at zero cost or a very small cost in terms of output.
主题	Monetary Economics and Fluctuations
关键词	Bayesian estimation Epidemiological models Causality Policy interventions
URL	https://cepr.org/publications/dp15951
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/544941
推荐引用方式 GB/T 7714	Jonas Arias,Jesus Fernandez-Villaverde,Juan Francisco Rubio-Ramírez,et al. DP15951 Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs. 2021.