Hello,
I am working on my thesis, with the topic "Factors determining life satisfaction in the USA". I was given a dataset of 265 variables and more than 2000 observations. My aim is to compare the effects of economics and social problems (which are not already variables in the dataset) on the happiness.
I would like to first, using LASSO (lasso2 depvar indepvar, lic(aic) ) to choose the suitable variables for the regression. And then, from the selected variables, I use PCA to combine them to only 10 factors, including economics and social problems). The last step is to build a model from these factors, with happiness as the dependent variables.
In this case, LASSO selected 98 variables, and the number is too large to build a model. Therefore, I would like to use PCA to both reduce the dimension and call out the needed latent variables.
May I ask is this an acceptable method to combine LASSO and PCA? If not, could you please suggest me a better method?

Thank you very much!