EXAMPLEFIVE.RPF

EXAMPLEFIVE.RPF is an example for the Introduction. This works with cross section data, showing the use of the SMPL option to restrict subsamples based upon the value of a series, and use of SCATTER to generate an x-y graph.

Full Program

open data wages1.dat
data(format=prn,org=columns) 1 3294 exper male school wage
*
* Do basic statistics on the two subsamples. The first is where "male" is
* non-zero, the second where .not.male is non-zero, that is, where male
* itself is zero.
*
stats(smpl=male) wage
stats(smpl=.not.male) wage
*
* The regression on constant and the male dummy will give the same type
* of information in a form which will usually be easier to interpret. The
* coefficient on the intercept will be the same as the mean for the
* females, while the coefficient on the male dummy is the difference
* between the mean for males and the mean for females.
*
linreg wage
# constant male
*
* Adds school and exper to the regression and test the joint
* significance of the two additional variables.
*
linreg wage
# constant male school exper
*
* This is generated by the Regression Tests Wizard
*
test(zeros)
# 3 4
*
* The same test can also be done using EXCLUDE
*
exclude
# school exper
*
* Generate the fitted values from the original regression and do an
* Actual-Fitted graph.
*
linreg wage
# constant school
prj wagefit
*
scatter(style=symbols,overlay=lines,ovsame,$
vlabel="Hourly Wages",hlabel="Years of School") 2
# school wage
# school wagefit

Output

Statistics on Series WAGE

Observations 1725 Skipped/Missing 1569

Sample Mean 6.313021 Variance 12.242031

Standard Error 3.498861 SE of Sample Mean 0.084243

t-Statistic (Mean=0) 74.938512 Signif Level (Mean=0) 0.000000

Skewness 1.921402 Signif Level (Sk=0) 0.000000

Kurtosis (excess) 8.845542 Signif Level (Ku=0) 0.000000

Jarque-Bera 6685.148147 Signif Level (JB=0) 0.000000

Statistics on Series WAGE

Observations 1569 Skipped/Missing 1725

Sample Mean 5.146924 Variance 8.272740

Standard Error 2.876237 SE of Sample Mean 0.072613

t-Statistic (Mean=0) 70.881766 Signif Level (Mean=0) 0.000000

Skewness 1.977027 Signif Level (Sk=0) 0.000000

Kurtosis (excess) 10.989324 Signif Level (Ku=0) 0.000000

Jarque-Bera 8917.135961 Signif Level (JB=0) 0.000000

Linear Regression - Estimation by Least Squares

Dependent Variable WAGE

Usable Observations 3294

Degrees of Freedom 3292

Centered R^2 0.0317459

R-Bar^2 0.0314517

Uncentered R^2 0.7639932

Mean of Dependent Variable 5.7575850178

Std Error of Dependent Variable 3.2691857840

Standard Error of Estimate 3.2173642756

Sum of Squared Residuals 34076.917047

Regression F(1,3292) 107.9338

Significance Level of F 0.0000000

Log Likelihood -8522.2280

Durbin-Watson Statistic 1.8662

Variable Coeff Std Error T-Stat Signif

************************************************************************************

1. Constant 5.1469238679 0.0812248211 63.36639 0.00000000

2. MALE 1.1660972915 0.1122421588 10.38912 0.00000000

Linear Regression - Estimation by Least Squares

Dependent Variable WAGE

Usable Observations 3294

Degrees of Freedom 3290

Centered R^2 0.1325877

R-Bar^2 0.1317968

Uncentered R^2 0.7885729

Mean of Dependent Variable 5.7575850178

Std Error of Dependent Variable 3.2691857840

Standard Error of Estimate 3.0461431195

Sum of Squared Residuals 30527.870207

Regression F(3,3290) 167.6302

Significance Level of F 0.0000000

Log Likelihood -8341.0906

Durbin-Watson Statistic 1.9051

Variable Coeff Std Error T-Stat Signif

************************************************************************************

1. Constant -3.380018181 0.464976503 -7.26922 0.00000000

2. MALE 1.344368629 0.107675888 12.48533 0.00000000

3. SCHOOL 0.638797702 0.032795844 19.47801 0.00000000

4. EXPER 0.124825450 0.023762761 5.25299 0.00000016

Null Hypothesis : The Following Coefficients Are Zero

SCHOOL

EXPER

F(2,3290)= 191.24105 with Significance Level 0.00000000

Null Hypothesis : The Following Coefficients Are Zero

SCHOOL

EXPER

F(2,3290)= 191.24105 with Significance Level 0.00000000

Linear Regression - Estimation by Least Squares

Dependent Variable WAGE

Usable Observations 3294

Degrees of Freedom 3292

Centered R^2 0.0798020

R-Bar^2 0.0795225

Uncentered R^2 0.7757067

Mean of Dependent Variable 5.7575850178

Std Error of Dependent Variable 3.2691857840

Standard Error of Estimate 3.1365065226

Sum of Squared Residuals 32385.620064

Regression F(1,3292) 285.4910

Significance Level of F 0.0000000

Log Likelihood -8438.3863

Durbin-Watson Statistic 1.8265

Variable Coeff Std Error T-Stat Signif

************************************************************************************

1. Constant -0.722506571 0.387391365 -1.86506 0.06226247

2. SCHOOL 0.557161695 0.032975021 16.89648 0.00000000

Graph