Adding more variables to a model usually increases its accuracy. However, without adequate analysis it could also lead to curve fitting.
Another question (How much data is needed to validate a short-horizon trading strategy?) received answers related to the statistical significance of the standard error of the model. However, I wonder if anyone has results (or analysis) of what should be the ratio of sample data to dimensions used in a model. My intuition has led me to use at least 30 times more sample data points than variables implemented as dimensions but I am not happy with this approach.
I guess that this would depend on the characteristics of the model (it would be different for linear regressions, SVM, non-linear models, etc. and also dependent on the relationships among the variables used) but is there a general framework for estimating this?