4.67; this is the value of an F2,82 random variable (n = 88, k = 3), and the associated p-value is.012. This is evidence of functional form misspecification in (6.23). The RESET statistic in (6.25)is 2.56, with p-value 5 .084. Thus, we do not reject (6.25) at the 5% significance level (althoughwe would at the 10% level). On the basis of RESET, the log-log model in (6.25) is preferred.One was rejected by RESET, while the other was not (at least at the 5% level). Often, thingsare not so simple. A drawback with RESET is that it provides no real direction on how to proceedif the model is rejected. Rejecting (9.4) by using RESET does not immediately suggest that (6.25)is the next step. Equation (9.5) was estimated because constant elasticity models are easy tointerpret and can have nice statistical properties. In this example, it so happens that it passes thefunctional form test as well.Some have argued that RESET is a very general test for model misspecification, includingunobserved omitted variables and heteroskedasticity. Unfortunately, such use of RESET is largelymisguided. It can be shown that RESET has no power for detecting omitted variables wheneverthey have expectations that are linear in the included independent variables in the model [seeWooldridge (1995) for a precise statement]. Further, if the functional form is properly specified,RESET has no power for detecting heteroskedasticity. The bottom line is that RESET is afunctional form test, and nothing more.Example in generating RAMSEY RESET Test through EviewsExample:Let us have an example on the effect of labor and capital inputson gross domestic product