How to interpret log differences in a partial log-log regression

pixed

New Member
I'm currently trying to understand the relationship between firm performance and various independent variables (e.g. firm size, firm profits..). Now, the regression I'm estimating looks like the following:

Δlog(firm_performance) = α + β1 Δlog(firm_size) + β2(other_variable) + ε

Where Δ represents the difference between the observation at time t and the observation at time t-1 and the logarithm of other_variable has not been taken.
How do I correctly read and understand the results if "other_variable" is found to be positively and significantly correlated with "firm_performance"? Does an increase in "other_variable" cause an increase in "firm_performance"? Or does an increase in "other_variable" cause an increase in the variance of "firm_performance" (Δfirm_performance)?

Last edited:

fed2

Active Member
'sup. im going with, an increase in other_variable gives an exp(B2) fold increase in the geometric mean fold increase, year on year. so its a ratio of ratios type thing. i could be wrong about that but test it out.