## * * * * T H E P A R K T E S T * * * *

1. Run an OLS regression and saved the residuals (e)
2. Square the residuals: ESQ=e2
3. Take the natural log of ESQ: LNESQ=ln(ESQ)
4. Take the natural log of variable which has suspected heteroskedasticity: LNX = ln(X)
5. Run the OLS model: LNESQ = f(constant, LNX)
6. If LNX is significant then heteroskedasticity is present
SPSS OUTPUT
```
Equation Number 1    Dependent Variable..   LNESQ

********************************************************
NOTE: The dependent variable is the natural log of the
squared residuals
********************************************************

Block Number  1.  Method:  Enter      LNAGE

********************************************************
NOTE: The independent variable is the natural log of age
(the variable that appears to be heteroskedastic)
********************************************************

R Square             .05292
Standard Error      2.37106

Analysis of Variance
DF      Sum of Squares      Mean Square
Regression           1           613.13367        613.13367
Residual          1952         10973.96564          5.62191

F =     109.06148       Signif F =  .0000

------------------ Variables in the Equation ------------------

Variable              B        SE B       Beta         T  Sig T

LNAGE         -1.483884     .142090   -.230033   -10.443  .0000
(Constant)    17.451896     .534475               32.652  .0000

*********************************************************************
NOTE: The T-stat on LNAGE is "greater than 2" indicating that
heteroskedasticity is present.
*********************************************************************

```