PROC PANEL: The Airline Cost Data: Further Analysis

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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