Fluorite | Level 6

## Dummy Variable

Hi Community,

Every time I run Rogers standard errors model, I get .... for the dummy variable yr12, although my sample has data on yr12. Your assistance is highly appreciated.

DATA Aac; * Full sample;
set Aac;
proc surveyreg data=Aac;
cluster year;

model Aac_int = EB_Aac1 NewReg LagEarnings2 LagAac_int
yr00 yr01 yr02 yr03 yr04 yr05 yr06 yr07 yr08 yr09
yr10 yr11 yr12 yr13 yr14 yr15 yr16 yr17 /ADJRSQ ; run;

1 ACCEPTED SOLUTION

Accepted Solutions
Diamond | Level 26

## Re: Dummy Variable

In PROC SURVEYREG, there's no need to create your own dummy variables, the CLASS statement will do that for you behind the scenes.

In your case, the regression coefficient of yr12 cannot be estimated because it is not independent of the other variables yr00-yr17. This is not an error, this is the way SAS (and probably most other statistical programs) handle the situation. Consider the simple case where you have dummy variables for male and female. If you know the dummy variable (0 or 1) for male, then you also know the dummy variable 0 or 1 for female, these are completely dependent on one another and so a dummy variable for female adds no new information. The same is true for yr12 ... if you know yr00-yr11 and yr13-yr17, then you know exactly yr12, it adds no new information.

Rather than look at the regression coefficients for your dummy variables, you want to look at the least squares means for your dummy variables using the LSMEANS statement. This will overcome the problems mentioned above.

--
Paige Miller
6 REPLIES 6
Diamond | Level 26

## Re: Dummy Variable

Show us the output you are getting. Copy the text and then paste it into the window that appears when you click on the {i} icon.

--
Paige Miller
Fluorite | Level 6

## Re: Dummy Variable

```27   proc surveyreg data=Aac;
28               cluster year;
29
30       model Aac_int = EB_Aac1 NewReg LagEarnings2 LagAac_int
31                       yr00 yr01 yr02 yr03 yr04 yr05 yr06 yr07 yr08 yr09
32                       yr10 yr11 yr12 yr13 yr14 yr15 yr16 yr17 /ADJRSQ ; run;

NOTE: PROCEDURE SURVEYREG used (Total process time):
real time           0.04 seconds
cpu time            0.03 seconds

The SAS System

The SURVEYREG Procedure

Regression Analysis for Dependent Variable AAC_int

Data Summary
Number of Observations 840
Mean of AAC_int 0.0034912
Sum of AAC_int 2.93261

Design Summary
Number of Clusters 19

Fit Statistics
R-Square 0.7176
Root MSE 0.05224
Denominator DF 18

Tests of Model Effects
Effect Num DF F Value Pr > F
Model 3 415.40 <.0001
Intercept 0 . .
EB_Aac1 1 1100.31 <.0001
NewReg 0 . .
LagEarnings2 1 360.67 <.0001
LagAac_int 1 0.06 0.8089
yr00 0 . .
yr01 0 . .
yr02 0 . .
yr03 0 . .
yr04 0 . .
yr05 0 . .
yr06 0 . .
yr07 0 . .
yr08 0 . .
yr09 0 . .
yr10 0 . .
yr11 0 . .
yr12 0 . .
yr13 1 211.22 <.0001
yr14 1 20.55 0.0003
yr15 1 10.97 0.0039
yr16 1 4.24 0.0543
yr17 1 83.39 <.0001

Note: The denominator degrees of freedom for the F tests is 18.

Estimated Regression Coefficients
Parameter Estimate Standard
Error t Value Pr > |t|
Intercept 0.0470502 0.00306833 15.33 <.0001
EB_Aac1 -0.7818615 0.02357066 -33.17 <.0001
NewReg -0.0235215 0.00068571 -34.30 <.0001
LagEarnings2 0.5692596 0.02997476 18.99 <.0001
LagAac_int -0.0048703 0.01984826 -0.25 0.8089
yr00 -0.0041067 0.00192654 -2.13 0.0471
yr01 -0.0288705 0.00080455 -35.88 <.0001
yr02 -0.0223174 0.00034868 -64.01 <.0001
yr03 -0.0165263 0.00037033 -44.63 <.0001
yr04 -0.0008246 0.00078551 -1.05 0.3077
yr05 -0.0063398 0.00140053 -4.53 0.0003
yr06 -0.0198335 0.00166022 -11.95 <.0001
yr07 -0.0344668 0.00153419 -22.47 <.0001
yr08 -0.0317944 0.00107077 -29.69 <.0001
yr09 -0.0450443 0.00144926 -31.08 <.0001
yr10 -0.0199334 0.00108137 -18.43 <.0001
yr11 -0.0079761 0.00061658 -12.94 <.0001
yr12 0.0000000 0.00000000 . .
yr13 -0.0069506 0.00047824 -14.53 <.0001
yr14 -0.0021293 0.00046976 -4.53 0.0003
yr15 -0.0015849 0.00047855 -3.31 0.0039
yr16 0.0006426 0.00031218 2.06 0.0543
yr17 -0.0024150 0.00026446 -9.13 <.0001

Note: The degrees of freedom for the t tests is 18.
Matrix X'X is singular and a generalized inverse was used to solve the normal equations. Estimates are not unique.

```
Fluorite | Level 6

## Re: Dummy Variable

I also attached the file in my previous message if you like to run the model.

Thank you.

Fluorite | Level 6

## Re: Dummy Variable

The model works fine as I drop yr12.

Diamond | Level 26

## Re: Dummy Variable

In PROC SURVEYREG, there's no need to create your own dummy variables, the CLASS statement will do that for you behind the scenes.

In your case, the regression coefficient of yr12 cannot be estimated because it is not independent of the other variables yr00-yr17. This is not an error, this is the way SAS (and probably most other statistical programs) handle the situation. Consider the simple case where you have dummy variables for male and female. If you know the dummy variable (0 or 1) for male, then you also know the dummy variable 0 or 1 for female, these are completely dependent on one another and so a dummy variable for female adds no new information. The same is true for yr12 ... if you know yr00-yr11 and yr13-yr17, then you know exactly yr12, it adds no new information.

Rather than look at the regression coefficients for your dummy variables, you want to look at the least squares means for your dummy variables using the LSMEANS statement. This will overcome the problems mentioned above.

--
Paige Miller
Fluorite | Level 6

## Re: Dummy Variable

Thank you so much.

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