The PANEL Procedure

Example 19.3 The Airline Cost Data: Further Analysis

Using the same data as in Example 19.2, you further investigate the 'true' effect of fuel prices. Specifically, you run the FixOne model, ignoring time effects. You specify the following statements in PROC PANEL to run this model:

```   proc panel data=airline;
id i t;
model lC = lQ lPF LF / fixone;
run;
```

The preceding statements result in Output 19.3.1. The fit seems to have deteriorated somewhat. The SSE rises from 0.1768 to 0.2926.

Output 19.3.1 The Airline Cost Data—Fit Statistics
The PANEL Procedure
Fixed One Way Estimates

Dependent Variable: lC Log transformation of costs

Fit Statistics
SSE 0.2926 DFE 81
MSE 0.0036 Root MSE 0.0601
R-Square 0.9974

You still reject poolability based on the F test in Output 19.3.2 at all accepted levels of significance.

Output 19.3.2 The Airline Cost Data—Test for Fixed Effects
F Test for No Fixed Effects
Num DF Den DF F Value Pr > F
5 81 57.74 <.0001

The parameters change somewhat dramatically as shown in Output 19.3.3. The effect of fuel costs comes in very strong and significant. The load factor’s coefficient increases, although not as dramatically. This suggests that the fixed time effects might be proxies for both the oil shocks and deregulation.

Output 19.3.3 The Airline Cost Data—Parameter Estimates
Parameter Estimates
Variable DF Estimate Standard Error t Value Pr > |t| Label
CS1 1 -0.08708 0.0842 -1.03 0.3041 Cross Sectional Effect 1
CS2 1 -0.12832 0.0757 -1.69 0.0940 Cross Sectional Effect 2
CS3 1 -0.29599 0.0500 -5.92 <.0001 Cross Sectional Effect 3
CS4 1 0.097487 0.0330 2.95 0.0041 Cross Sectional Effect 4
CS5 1 -0.06301 0.0239 -2.64 0.0100 Cross Sectional Effect 5
Intercept 1 9.79304 0.2636 37.15 <.0001 Intercept
lQ 1 0.919293 0.0299 30.76 <.0001 Log transformation of quantity
lPF 1 0.417492 0.0152 27.47 <.0001 Log transformation of price of fuel
LF 1 -1.07044 0.2017 -5.31 <.0001 Load Factor (utilization index)

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