Problems with r2 as a goodness offif4-6 Measure 1. R is defined in terms of variation about the mean of y so that if a model is reparameterised (rearranged) and the dependent variable changes, R will change. 2. R never falls if more regressors are added to the regression, e.g. consider Regression 1: y=B1+B2x2t+Bx3t+ut Regression 2: y B1+B22+B3x3+B4 4t R2 will always be at least as high for regression 2 relative to regression 1 3. R2 quite often takes on values of 0.9 or higher for time series regressions.4-6 Problems with R2 as a Goodness of Fit Measure 1. R2 is defined in terms of variation about the mean of y so that if a model is reparameterised (rearranged) and the dependent variable changes, R2 will change. 2. R2 never falls if more regressors are added to the regression, e.g. consider: Regression 1: yt = 1 + 2x2t + 3x3t + ut Regression 2: y = 1 + 2x2t + 3x3t + 4x4t + ut R2 will always be at least as high for regression 2 relative to regression 1. 3. R2 quite often takes on values of 0.9 or higher for time series regressions