Measuring dynamic pandemic-related policy effects: A time-varying parameter multi-level dynamic factor model approach

Abstract

This study evaluates the dynamic impact of various policies adopted by U.S. states, including social distancing, financial assistance, and vaccination policies. We propose a time-varying parameter multilevel dynamic factor model (TVP-MDFM) to improve the model’s accuracy for evaluating the dynamic policy effect. The estimation is based on the Bayesian shrinkage method jointly with the Markov chain Monte Carlo (MCMC) algorithm that combines model selection and parameter estimation into the same iterative sampling process. The advantages and reliability of the TVP-MDFM are explored using simulation studies and robustness tests. The main empirical results highlight that the direct causal effect of the social distancing policy is more significant than the indirect effect mediated through human behavior. We also find income heterogeneity in financial assistance policies. Moreover, we provide evidence that banning vaccination certification by legislation is a stronger driver of the new case rate than executive orders during the Omicron dominance.