Hi All. I'm new to this forum but I have a background in stats and in business/social science research.
I'm in the early stages of designing a research study that will analyze a large dataset for about 100 publicly traded companies. There will be about 10-20 independent variables for each company, each relating to business activities and performance metrics for a given month. I am trying to find out if any of these are predictive of business outcomes - revenue, stock price, etc.
I will have about 18 months of data for each company, showing the month-by-month results for each of the 10-20 variables, along with accompanying stock price, revenue data, etc.
I understand how to run regressions for each one of the 100 companies individually. My questions is this: how do I agglomerate or combine the results across ALL 100 companies, to get a overall result which will presumably be more powerful?
(Apologies if I'm not giving enough detail here, happy to answer any questions!)
I'm in the early stages of designing a research study that will analyze a large dataset for about 100 publicly traded companies. There will be about 10-20 independent variables for each company, each relating to business activities and performance metrics for a given month. I am trying to find out if any of these are predictive of business outcomes - revenue, stock price, etc.
I will have about 18 months of data for each company, showing the month-by-month results for each of the 10-20 variables, along with accompanying stock price, revenue data, etc.
I understand how to run regressions for each one of the 100 companies individually. My questions is this: how do I agglomerate or combine the results across ALL 100 companies, to get a overall result which will presumably be more powerful?
(Apologies if I'm not giving enough detail here, happy to answer any questions!)