Hello Statalist Forum,

I am looking at the industry and country effect in 16 countries and 10 industries and how these effects emerge over time. My dataset consists of daily rate of returns (RoR) from 1975 to 2018. To get the country and industry effect I created dummy variables for each country and each industry. I want to run a weekly/yearly cross sectional regression with the dependent variable being the rate of return and the independent variables are the dummy variables created. This is done for each country and each industry so that I would like to do

1.
Code:
regress RoR MatsD GdsD SvsD FinD HealthD IndD OilD TechD TeleD UtilsD AustriaD BelgiumD DenmarkD FinlandD FranceD GermanyD GreeceD IrelandD ItalyD NetherlandsD NorwayD PortugalD SpainD SwedenD SwitzerlandD UnitedKingdomD
but by year and Country/Industry. This should result in a time series of the coefficients produced by the cross sectional regression.

To do this I used the following code:

2.
Code:
bys year Country: asreg RoR MatsD GdsD SvsD FinD HealthD IndD OilD TechD TeleD UtilsD AustriaD BelgiumD DenmarkD FinlandD FranceD GermanyD GreeceD IrelandD ItalyD NetherlandsD NorwayD PortugalD SpainD SwedenD SwitzerlandD UnitedKingdomD
Where all variables ending with a D are dummy variables for the country or industry. Because I never worked with this command before I am not sure if it is the proper way to do the cross sectional analysis or if it even yields the right coefficients.

Furthermore, this yield equally weighted estimates. Is there a way to include value weighted estimates? Is there a way to include
Code:
[aweight=weight]
in the second regression?