I have a database with GDP and energy consumption by country (100 countries). This is yearly data from 1900 to 2015. I would like to perform a global varmax model for the whole scope.
Would you have any idea for doing that ?
Sound like an interesssting study.
At your fist attempt you probably will use only a couple of countries.
First you will need to put your data into the following format:
Year GDP_Country1 Energy_Country1 GDP_Country2 Energy_Country2
1990 10 6 20 8
1991 12 7 22 10
And then... it depends. What is the goal?
Do you want to treat GDP variables as explanatory? Or all variables are Y-s?
Probably there will be high correlation beween variables... but as this is a very long time period, those correlations can change over time. To assess the degree of cointegration will be a challege...
Maybe you could try PROC SSM (more flexible handling changing correlations).
Thanks for your answer. Actually, the model I'd like to perform, in one consistent step, is a two-fixed effect for country and time and a model var ecm on energy consumption and GDP.
What I want to assess is the long run relationship between energy consumption and GDP and Granger causalities.
It can be easily perfomed in Eviews and I am surprised it couldn't be computed in SAS (probably due to my lack of knowledge).
I hope it clarifies. I gonna have a look in the SSM procedure.
Two-way fixed effects models are available with PROC PANEL.
But I don't know how to incorporate two-way effects, ecm and causality test in one step (is it possible?).
Maybe VARMAX with country_dummy variables... But then it is just one-way.
The closest example (panel data) in PROC SSM documentation:
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