Which technique can be used when T=4 and N=29?
Dear all Statalisters,
I am working on economic impacts of artificial intelligence for my PhD dissertation. Now, i have two models. The first one is employment, the other one is growth model.
In employment model, dependent variable is employment rate independent variable is artificial intelligence and i have four control variables. For this model, I used GMM and i got statistically significant model. However, in Thesis Monitoring Committee, they asked me that if i'm sure whether the model is true or not. Then, i doubted myself. I guess that i have to mount an argument about my model or must change econometric method.
Also, there is presence of cross sectional dependence for all variables. So, ı thought that ı cannot use random or fixed effect because of presence of cross sectional dependence. Maybe, ı am wrong.
At that, which method/technique can be suitable for my dataset (T=4 N=29)?
Thanks for your answer in advance.
I am working on economic impacts of artificial intelligence for my PhD dissertation. Now, i have two models. The first one is employment, the other one is growth model.
In employment model, dependent variable is employment rate independent variable is artificial intelligence and i have four control variables. For this model, I used GMM and i got statistically significant model. However, in Thesis Monitoring Committee, they asked me that if i'm sure whether the model is true or not. Then, i doubted myself. I guess that i have to mount an argument about my model or must change econometric method.
Also, there is presence of cross sectional dependence for all variables. So, ı thought that ı cannot use random or fixed effect because of presence of cross sectional dependence. Maybe, ı am wrong.
At that, which method/technique can be suitable for my dataset (T=4 N=29)?
Thanks for your answer in advance.
