Interpretation of logarithms in a regression

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Taken from Introduction to Econometrics from Stock and Watson, 2003, p. 215:

Y=B0 + B1*ln(X) + u ~ A 1% change in X is associated with a change in Y of 0.01*B1

ln(Y)=B0 + B1*X + u ~ A change in X by one unit (∆X=1) is associated with a (exp(B1) - 1)*100 % change in Y

ln(Y)=B0 + B1*ln(X) + u ~ A 1% change in X is associated with a B1% change in Y, so B1 is the elasticity of Y with respect to X.

Out-of sample test

predict can be used to make in-sample or out-of-sample predictions:

6)  predict calculates the requested statistic for all possible

observations, whether they were used in fitting the model or not.

predict does this for the standard options (1) through (3) and

generally does this for estimator-specific options (4).

7)  predict newvar if e(sample), ...  restricts the prediction to the

estimation subsample.

8)  Some statistics make sense only with respect to the estimation

subsample.  In such cases, the calculation is automatically

restricted to the estimation subsample, and the documentation for

the specific option states this.  Even so, you can still specify

if e(sample) if you are uncertain.

9)  predict can make out-of-sample predictions even using other

datasets.  In particular, you can

. use ds1

(fit a model)

. use two              /* another dataset */

. predict yhat, ...    /* fill in the predictions */”