Using the same data as in Example 20.2, 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 20.3.1. The fit seems to have deteriorated somewhat. The SSE rises from 0.1768 to 0.2926.
You still reject poolability based on the F test in Output 20.3.2 at all accepted levels of significance.
The parameters change somewhat dramatically as shown in Output 20.3.3. 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 20.3.3: The Airline Cost Data—Parameter Estimates
Parameter Estimates | ||||||
---|---|---|---|---|---|---|
Variable | DF | Estimate | Standard Error |
t Value | Pr > |t| | Label |
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) |