CPANEL Procedure

The Christenson Associates airline data are a frequently cited data set (Greene 2000). The data measure the costs, prices of inputs, and utilization rates for six airlines from 1970 to 1984. This example analyzes the log transformations of cost (variable C), quantity (variable Q), and price (variable PF), and the untransformed load factor (variable LF). You speculate the following model,

where the are airline effects. The actual model in the original, untransformed variables is highly nonlinear:

The following statements create the data and perform the necessary log transformations. These statements assume that your libref is named mylib, but you can substitute any appropriately defined libref.

data mylib.airline;
   input Obs Airline T C Q PF LF;
   Year = T + 1969;
   lC   = log(C);
   lQ   = log(Q);
   lPF  = log(PF);
   label lC  = "Log Transformation of Costs";
   label lQ  = "Log Transformation of Quantity";
   label lPF = "Log Transformation of Price of Fuel";
   label LF  = "Load Factor (utilization index)";
datalines;
 1    1     1    1140640    0.95276     106650    0.53449
 2    1     2    1215690    0.98676     110307    0.53233
 3    1     3    1309570    1.09198     110574    0.54774
 4    1     4    1511530    1.17578     121974    0.54085
 5    1     5    1676730    1.16017     196606    0.59117

   ... more lines ...   

The following statements fit a one-way fixed-effects model:

proc cpanel data =  mylib.airline;
   id Airline Year;
   model lC = lQ lPF LF / fixone printfixed;
run;

Output 11.1.1 provides a model and data description. There are six cross sections and 15 time points.

The R-square and degrees of freedom can be seen in Output 11.1.2. The R-square statistic nearly 1, indicating a reasonable fit. The error degrees of freedom is derived from 90 observations minus 5 cross sections, minus 4 regressors.

The F test for fixed effects is shown in Output 11.1.3. You easily reject the null hypothesis of poolability. There are significant effects due to airlines, and it would be unreasonable to perform pooled OLS regression that ignores these effects.

The PRINTFIXED option in the MODEL statement provides estimates of the airline effects (which are not displayed by default). Looking at the parameters, only two airlines (3 and 4) are significantly different from average. Quantity and fuel price have positive effects on cost, but load factors negatively affect costs. Because cost, quantity, and fuel price are log-transformed, the coefficients for quantity and price are interpreted as elasticities of cost. The coefficient for (log) fuel price is 0.417, meaning that you would associate a 10% increase in fuel price with a 4.17% increase in costs.

You suspect that there might be other factors at play, and so you augment your model to include time effects. The following statements fit a two-way model, a model with both airline and time effects:

proc cpanel data =  mylib.airline;
   id Airline Year;
   model lC = lQ lPF LF / fixtwo printfixed;
run;

The F test and parameter estimates for the two-way model are provided in Output 11.1.5.

Only a few of the time effects are significant, but there is an overall time trend of increasing costs. The time period of the data spans the OPEC oil embargoes and the dissolution of the Civil Aeronautics Board (CAB). These are two possible explanations for the rising costs.

A surprising result is that the fuel cost is not significant in the two-way model. If the time effects are proxies for the effect of the oil embargoes, then the effect of fuel price might be subsumed by the time effects. If the time dummy variables are proxies for the dissolution of the CAB, then the effect of load factors is not precisely estimated.