* OLS Model for US Coffee Consumption
*
* Keywords:
* regression, ols, log, coffee, r, squared
*
* Description:
* We illustrate how to estimate Linear and Log OLS Regression Models for
* US Coffee Consumption and make comparable R squared values
*
* Author(s):
* Skif Pankov
*
* Source:
* Damodar N. Gujarati and Dawn C. Porter, Basic Econometrics - 5th Edition
* McGraw-Hill International Edition, Chapter 7, Example 7.2 (page 198)
*
sample 1 11
* Reading the datafile and naming the variables
read(data_7.2.shd) y x
* Generating logs of x and y
genr lnx = log(x)
genr lny = log(y)
* Running an OLS regression of y on x, stating to display residual statistics and to
* save predicted values into a variable py
ols y x / rstat predict = py
* Storing the R squared of the previous regression as rlin
gen1 rlin = $r2
* Running an OLS regression of lny on lnx, stating to display residual statistics
* and to save predicted values into a variable plny
ols lny lnx / rstat predict = plny
* Storing the R squared of the previous regression as rlog
gen1 rlog = $r2
* Generating the exponent of predicted logs of coffee consumption and a log
* of predicred coffee consumption
genr invplny = exp(plny)
genr lnpy = log(py)
* Running OLS models of the actual on estimated coffee consumptions and
* saving R squared
ols y invplny
gen1 arlog = $R2
ols lny lnpy
gen1 arlin = $R2
* Displaying the values of R squared, which can be compared
print rlin arlin
print rlog arlog
stop