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10.1016/j.jeconom.2021.01.001 http://scihub22266oqcxt.onion/10.1016/j.jeconom.2021.01.001 33519026!7833926!33519026     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Econom 2023 ; 232 (1): 70-86 Nephropedia Template TP {{tp|p=33519026|t=2023. How to go viral: A COVID-19 model with endogenously time-varying parameters |pdf=|usr=}}{{33519026}} gab.com Text How to go viral: A COVID-19 model with endogenously time-varying parameters JO: J Econom http://www.kidney.de/pget.php?&tw=1&pmid=33519026 #FOAvip We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics. Twit Text FOAVip How to go viral: A COVID-19 model with endogenously time-varying parameters ????J Econom http://www.kidney.de/pget.php?&tw=1&pmid=33519026 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33519026 #FOAvip English Wikipedia <ref>{{Cite pmid|33519026}}</ref> How to go viral: A COVID-19 model with endogenously time-varying parameters #MMPMID33519026 Ho P; Lubik TA; Matthes C J Econom 2023[Jan]; 232 (1): 70-86 PMID33519026show ga We estimate a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. Our Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics. äHow to go viral: A COVID-19 model with endogenously time-varying param(epmc).pdf DeepDyve Pubget Overpricing