TexasElectr: Production of Electricity in Texas

In pder: Panel Data Econometrics with R

Description

yearly observations of 10 firms from 1966 to 1983

number of observations : 180

number of time-series : 18

country : Texas

package : productionpanel

JEL codes: D24, C13, C51, C23, J31

Chapter : 02, 03

Usage

data(TexasElectr)

Format

A dataframe containing:

id

the firm identifier

year

the year, from 1966 to 1983

output

output

pfuel

price of fuel

plab

price of labor

pcap

price of capital

expfuel

expense in fuel

explab

expense in labor

expcap

expense in capital

Source

Journal of Applied Econometrics Data Archive : http://qed.econ.queensu.ca/jae/

References

Kumbhakar SC (1996) “Estimation of Cost Efficiency with Heteroscedasticity: An Application to Electric Utilities”, Journal of the Royal Statistical Society, Series D, 45, 319–335.

Horrace and Schmidt (1996) “Confidence Statements for Efficiency Estimates From Stochastic Frontier Models”, Journal of Productity Analysis, 7, 257–282, doi: 10.1007/BF00157044 .

Horrace and Schmidt (2012) “Multiple Comparisons with the Best, with Economic Applications”, Journal of Applied Econometrics, 15(1), 1–26, doi: 10.1002/(SICI)1099-1255(200001/02)15:1<1::AID-JAE551>3.0.CO;2-Y .

Examples

#### Example 2-6

## ------------------------------------------------------------------------
data("TexasElectr", package = "pder")
library("plm")
TexasElectr$cost <- with(TexasElectr, explab + expfuel + expcap)
TE <- pdata.frame(TexasElectr)
summary(log(TE$output))
ercomp(log(cost) ~ log(output), TE)
models <- c("within", "random", "pooling", "between")
sapply(models, function(x)
       coef(plm(log(cost) ~ log(output), TE, model = x))["log(output)"])

#### Example 3-2

## ------------------------------------------------------------------------
data("TexasElectr", package = "pder")

if (requireNamespace("dplyr")){
    library("dplyr")
    TexasElectr <- mutate(TexasElectr,
                          pf = log(pfuel / mean(pfuel)),
                          pl = log(plab / mean(plab)) - pf,
                          pk = log(pcap / mean(pcap)) - pf)

## ------------------------------------------------------------------------
    TexasElectr <- mutate(TexasElectr, q = log(output / mean(output)))

## ------------------------------------------------------------------------
    TexasElectr <- mutate(TexasElectr,
                          C = expfuel + explab + expcap,
                          sl = explab / C,
                          sk = expcap / C,
                          C = log(C / mean(C)) - pf)
    
## ------------------------------------------------------------------------
    TexasElectr <- mutate(TexasElectr,
                          pll = 1/2 * pl ^ 2,
                          plk = pl * pk,
                          pkk = 1/2 * pk ^ 2,
                          qq = 1/2 * q ^ 2)

## ------------------------------------------------------------------------
    cost <- C ~ pl + pk + q + pll + plk + pkk + qq
    shlab <- sl ~ pl + pk
    shcap <- sk ~ pl + pk

## ------------------------------------------------------------------------
    R <- matrix(0, nrow = 6, ncol = 14)
    R[1, 2] <- R[2, 3] <- R[3, 5] <- R[4, 6] <- R[5, 6] <- R[6, 7] <- 1
    R[1, 9] <- R[2, 12] <- R[3, 10] <- R[4, 11] <- R[5, 13] <- R[6, 14] <- -1

## ------------------------------------------------------------------------
    z <- plm(list(cost = C ~ pl + pk + q + pll + plk + pkk + qq,
                  shlab = sl ~ pl + pk,
                  shcap = sk ~ pl + pk),
             TexasElectr, model = "random",
             restrict.matrix = R)
    summary(z)
}

pder documentation built on Jan. 27, 2022, 1:12 a.m.