I think that a dsge model is a correct way to estimate the covid 19, I think that with these method can know the behavior of this illness can be estimated in the hospitalizations, deaths, etc.
We have used techniques like nowcasting and DSGE in our recent paper.
Analyzed, Macro economic factors like GDP, unemployment rate, repo rate, etc.. and its impact on economic recovery as it is critical and integral for recovery patterns.
We tried looking at it from the economic impact and consumption behavior shift of users by analyzing the impact of macroeconomic parameters on business and simulate all for COVID19.
We have built multiple ensemble models to analyze the impact of Pandemic (increase in infection rate, mortality rate, spread, peak) on macroeconomic parameters, which in-turn will impact stocks and commodity sales. Hence used simulations then to predict the impact of different businesses and their recovery pattern at national and market levels.
Used techniques like nowcasting and DSGE
refer the paper, should this give you some idea:
Article 3Ps- Path, Pace and Pattern of Recovery from COVID-19 for Di...
I have been modeling new cases, new deaths, among others related to covid 19, seeking to know when the peaks of the disease and outbreaks will be, the series that I have worked on are independent using ARMA models, however these are not endogenous. Searching the literature, I find that the SIR models are a small case of the DSGE models, that is, with these DSGE models I manage to model the covid 19 in an endogenous way, where I will capture the combinations of states that a person has. I think that to model this disease it is necessary to start from back to front, from deaths to new cases, because the information systems have temporary lags. I have thought to work with information from Colombia.
Hi, Yes of course I accept, I already have an article which will appear in December CGEM applied for cofid-19. But I have a little problem with the English language