Cost Data for US Airlines

Cost data for six US airlines in 1970–1984.

Usage

data("USAirlines")

Format

A data frame containing 90 observations on 6 variables.

firm

factor indicating airline firm.

year

factor indicating year.

output

output revenue passenger miles index number.

cost

total cost (in USD 1000).

price

fuel price.

load

average capacity utilization of the fleet.

Source

Online complements to Greene (2003). Table F7.1.

https://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

See also

Greene2003

Examples

data("USAirlines")

## Example 7.2 in Greene (2003)
fm_full <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + year + firm,
  data = USAirlines)
fm_time <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + year,
  data = USAirlines)
fm_firm <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + firm,
  data = USAirlines)
fm_no <- lm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load, data = USAirlines)

## Table 7.2
anova(fm_full, fm_time)
#> Analysis of Variance Table
#> 
#> Model 1: log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + 
#>     year + firm
#> Model 2: log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + 
#>     year
#>   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
#> 1     66 0.17257                                  
#> 2     71 1.03470 -5  -0.86213 65.945 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(fm_full, fm_firm)
#> Analysis of Variance Table
#> 
#> Model 1: log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + 
#>     year + firm
#> Model 2: log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + 
#>     firm
#>   Res.Df     RSS  Df Sum of Sq      F   Pr(>F)   
#> 1     66 0.17257                                 
#> 2     80 0.26815 -14 -0.095584 2.6112 0.004582 **
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(fm_full, fm_no)
#> Analysis of Variance Table
#> 
#> Model 1: log(cost) ~ log(output) + I(log(output)^2) + log(price) + load + 
#>     year + firm
#> Model 2: log(cost) ~ log(output) + I(log(output)^2) + log(price) + load
#>   Res.Df     RSS  Df Sum of Sq      F    Pr(>F)    
#> 1     66 0.17257                                   
#> 2     85 1.27492 -19   -1.1023 22.189 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

## alternatively, use plm()
library("plm")
usair <- pdata.frame(USAirlines, c("firm", "year"))
fm_full2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load,
  data = usair, model = "within", effect = "twoways")
fm_time2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load,
  data = usair, model = "within", effect = "time")
fm_firm2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load,
  data = usair, model = "within", effect = "individual")
fm_no2 <- plm(log(cost) ~ log(output) + I(log(output)^2) + log(price) + load,
  data = usair, model = "pooling")
pFtest(fm_full2, fm_time2)
#> 
#> 	F test for twoways effects
#> 
#> data:  log(cost) ~ log(output) + I(log(output)^2) + log(price) + load
#> F = 65.945, df1 = 5, df2 = 66, p-value < 2.2e-16
#> alternative hypothesis: significant effects
#> 
pFtest(fm_full2, fm_firm2)
#> 
#> 	F test for twoways effects
#> 
#> data:  log(cost) ~ log(output) + I(log(output)^2) + log(price) + load
#> F = 2.6112, df1 = 14, df2 = 66, p-value = 0.004582
#> alternative hypothesis: significant effects
#> 
pFtest(fm_full2, fm_no2)
#> 
#> 	F test for twoways effects
#> 
#> data:  log(cost) ~ log(output) + I(log(output)^2) + log(price) + load
#> F = 22.189, df1 = 19, df2 = 66, p-value < 2.2e-16
#> alternative hypothesis: significant effects
#> 

## More examples can be found in:
## help("Greene2003")