Hi everyone,

I have a dataset available which links the the Covid-19 situation to the change in port activity (expressed in difference of port calls = ship arrivals in ports) for any specific date between 01/01/2020 - 31/08/2020 and any given country. An example of dataset for the countries China & Belgium is attached to see the data formulation yourself. In the end the data set is a list per country and any date, given the country linked to indicators summarising the Covid-19 situation in that country for that particular date. More information about the indicators: The policy indicator reports a number which reflects the severity of the corresponding containment policy. The stricter a government’s policy is, the higher the representing indicator will be. C1, C2, and C6 are all three reported on a scale between zero and three (0 – 1 – 2 – 3). On the other hand, C3, C5, and C7 use a scale between zero and two (0 – 1 – 2), while C4 and C8 make use of scale between zero and four (0 – 1 – 2 – 3 – 4). The stringency index is reported by a percentage where a value close to 100% indicates the strictest situation one can imagine.

First, the stringency index is linked to the change in port calls by a single regression model.
Second, the main purpose is to check how much of the change in port calls is explained by the indicators and even more crucial, which indicator has the declarative value.

Now, I had some questions related about the second research question.
- Would it be best to use a panel regression model, or make use of multiple regression model?
- There are three different scales of containment policies used in the data set, does Stata automatically recognise difference between these scales?

Thank you in advance!
Dan