Example 20.5 Using the FLATDATA Statement

Sometimes the data can be found in compressed form, where each line consists of all observations for the dependent and independent variables for the cross section. To illustrate, suppose you have a data set with 20 cross sections where each cross section consists of observations for six time periods. Each time period has values for dependent and independent variables ... and .... The and variables represent other character and numeric variables that are constant across each cross section.

The observations for first five cross sections along with other variables are shown in Output 20.5.1. In this example, represents the cross section. The time period is identified by the subscript on the and variables; it ranges from 1 to 6.

Output 20.5.1 Compressed Data Set
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Obs i cs num X_1 X_2 X_3 X_4 X_5 X_6 Y_1 Y_2 Y_3 Y_4 Y_5 Y_6
1 1 CS1 -1.56058 0.40268 0.91951 0.69482 -2.28899 -1.32762 1.92348 2.30418 2.11850 2.66009 -4.94104 -0.83053 5.01359
2 2 CS2 0.30989 1.01950 -0.04699 -0.96695 -1.08345 -0.05180 0.30266 4.50982 3.73887 1.44984 -1.02996 2.78260 1.73856
3 3 CS3 0.85054 0.60325 0.71154 0.66168 -0.66823 -1.87550 0.55065 4.07276 4.89621 3.90470 1.03437 0.54598 5.01460
4 4 CS4 -0.18885 -0.64946 -1.23355 0.04554 -0.24996 0.09685 -0.92771 2.40304 1.48182 2.70579 3.82672 4.01117 1.97639
5 5 CS5 -0.04761 -0.79692 0.63445 -2.23539 -0.37629 -0.82212 -0.70566 3.58092 6.08917 3.08249 4.26605 3.65452 0.81826

Since the PANEL procedure cannot work directly with the data in compressed form, the FLATDATA statement can be used to transform the data. The OUT= option can be used to output transformed data to a data set.

proc panel data=flattest;
   flatdata indid=i tsname="t" base=(X Y)
            keep=( cs num seed )  / out=flat_out;
   id i t;
   model y = x / fixone noint;
run;

First, six observations for the uncompressed data set and results for the one-way fixed-effects model fitted are shown in Output 20.5.2 and Output 20.5.3.

Output 20.5.2 Uncompressed Data Set
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Obs I t X Y CS NUM
1 1 1 0.40268 2.30418 CS1 -1.56058
2 1 2 0.91951 2.11850 CS1 -1.56058
3 1 3 0.69482 2.66009 CS1 -1.56058
4 1 4 -2.28899 -4.94104 CS1 -1.56058
5 1 5 -1.32762 -0.83053 CS1 -1.56058
6 1 6 1.92348 5.01359 CS1 -1.56058

Output 20.5.3 Estimation with the FLATDATA Statement
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The PANEL Procedure
Fixed One Way Estimates
 
Dependent Variable: Y

Parameter Estimates
Variable DF Estimate Standard Error t Value Pr > |t| Label
CS1 1 0.945589 0.4579 2.06 0.0416 Cross Sectional Effect 1
CS2 1 2.475449 0.4582 5.40 <.0001 Cross Sectional Effect 2
CS3 1 3.250337 0.4579 7.10 <.0001 Cross Sectional Effect 3
CS4 1 3.712149 0.4617 8.04 <.0001 Cross Sectional Effect 4
CS5 1 5.023584 0.4661 10.78 <.0001 Cross Sectional Effect 5
CS6 1 6.791074 0.4707 14.43 <.0001 Cross Sectional Effect 6
CS7 1 6.11374 0.4649 13.15 <.0001 Cross Sectional Effect 7
CS8 1 8.733843 0.4580 19.07 <.0001 Cross Sectional Effect 8
CS9 1 8.916685 0.4587 19.44 <.0001 Cross Sectional Effect 9
CS10 1 8.913916 0.4614 19.32 <.0001 Cross Sectional Effect 10
CS11 1 10.82881 0.4580 23.64 <.0001 Cross Sectional Effect 11
CS12 1 11.40867 0.4603 24.79 <.0001 Cross Sectional Effect 12
CS13 1 12.8865 0.4585 28.10 <.0001 Cross Sectional Effect 13
CS14 1 13.37819 0.4580 29.21 <.0001 Cross Sectional Effect 14
CS15 1 14.72619 0.4579 32.16 <.0001 Cross Sectional Effect 15
CS16 1 15.58813 0.4580 34.04 <.0001 Cross Sectional Effect 16
CS17 1 17.77983 0.4579 38.83 <.0001 Cross Sectional Effect 17
CS18 1 17.9909 0.4618 38.96 <.0001 Cross Sectional Effect 18
CS19 1 18.87283 0.4583 41.18 <.0001 Cross Sectional Effect 19
CS20 1 19.40034 0.4579 42.37 <.0001 Cross Sectional Effect 20
X 1 2.010753 0.1217 16.52 <.0001