Final answer:
The statement is false; pooled OLS does not assume a constant regression function shape over time, but rather that individual-specific effects are not correlated with the explanatory variables.
Step-by-step explanation:
True or False: A key limitation of pooled OLS estimation is that it requires you to assume that the shape of the regression function is constant over time. This statement is false. Pooled OLS (Ordinary Least Squares) does not necessarily assume that the shape of the regression function is constant over time. Instead, what pooled OLS assumes is that individual-specific effects are not correlated with the other explanatory variables.
This technique essentially pools cross-sectional and time-series data but does not account for potential variations in the regression function across time. For analyses where the shape of the regression function is expected to change over time, techniques such as fixed effects or random effects models may be more appropriate.