The PANEL Procedure |
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.
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.
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.
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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