Tileries: Production of Tileries in Egypt

In pder: Panel Data Econometrics with R

Description

weeklyly observations of 25 firms from 1982 to 1983

number of observations : 483

number of time-series : 22

country : Egypt

package : panelproduction

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

Chapter : 01, 03

Usage

data(Tileries)

Format

A dataframe containing:

id

firm id

week

week (3 weeks aggregated)

area

one of "fayoum" and "kalyubiya"

output

output

labor

labor hours

machine

machine hours

Source

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

References

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 .

Seale J.L. (1990) “Estimating Stochastic Frontier Systems with Unbalanced Panel Data: the Case of Floor Tile Manufactories in Egypt”, Journal of Applied Econometrics, 5, 59–79, doi: 10.1002/jae.3950050105 .

Examples

#### Example 1-2

## ------------------------------------------------------------------------
data("Tileries", package = "pder")
library("plm")
coef(summary(plm(log(output) ~ log(labor) + machine, data = Tileries,
             subset = area == "fayoum")))

## ------------------------------------------------------------------------
coef(summary(plm(log(output) ~ log(labor) + machine, data = Tileries,
             model = "pooling", subset = area == "fayoum")))


#### Example 1-5

## ------------------------------------------------------------------------
data("Tileries", package = "pder")
til.fm <- log(output) ~ log(labor) + log(machine)
lm.mod <- lm(til.fm, data = Tileries, subset = area == "fayoum")

## ------------------------------------------------------------------------
if (requireNamespace("car")){
    library("car")
    lht(lm.mod, "log(labor) + log(machine) = 1")

## ------------------------------------------------------------------------
    library("car")
    lht(lm.mod, "log(labor) + log(machine) = 1", vcov=vcovHC)
}


#### Example 1-6

## ------------------------------------------------------------------------
plm.mod <- plm(til.fm, data = Tileries, subset = area == "fayoum")

## ------------------------------------------------------------------------
if (requireNamespace("car")){
    library("car")
    lht(plm.mod, "log(labor) + log(machine) = 1", vcov = vcovHC)
}

#### Example 3-1

## ------------------------------------------------------------------------
library(plm)
data("Tileries", package = "pder")
head(Tileries, 3)
pdim(Tileries)

## ------------------------------------------------------------------------
Tileries <- pdata.frame(Tileries)
plm.within <- plm(log(output) ~ log(labor) + log(machine), Tileries)
y <- log(Tileries$output)
x1 <- log(Tileries$labor)
x2 <- log(Tileries$machine)
lm.within <- lm(I(y - Between(y)) ~ I(x1 - Between(x1)) + I(x2 - Between(x2)) - 1)
lm.lsdv <- lm(log(output) ~ log(labor) + log(machine) + factor(id), Tileries)
coef(lm.lsdv)[2:3]
coef(lm.within)
coef(plm.within)

## ------------------------------------------------------------------------
tile.r <- plm(log(output) ~ log(labor) + log(machine), Tileries, model = "random")
summary(tile.r)

## ------------------------------------------------------------------------
plm.within <- plm(log(output) ~ log(labor) + log(machine),
                  Tileries, effect = "twoways")
lm.lsdv <- lm(log(output) ~ log(labor) + log(machine) +
                  factor(id) + factor(week), Tileries)
y <- log(Tileries$output)
x1 <- log(Tileries$labor)
x2 <- log(Tileries$machine)
y <- y - Between(y, "individual") - Between(y, "time") + mean(y)
x1 <- x1 - Between(x1, "individual") - Between(x1, "time") + mean(x1)
x2 <- x2 - Between(x2, "individual") - Between(x2, "time") + mean(x2)
lm.within <- lm(y ~ x1 + x2 - 1)
coef(plm.within)
coef(lm.within)
coef(lm.lsdv)[2:3]

## ------------------------------------------------------------------------
wh <- plm(log(output) ~ log(labor) + log(machine), Tileries,
          model = "random", random.method = "walhus",
          effect = "twoways")
am <- update(wh, random.method = "amemiya")
sa <- update(wh, random.method = "swar")
ercomp(sa)

## ------------------------------------------------------------------------
re.models <- list(walhus = wh, amemiya = am, swar = sa)
sapply(re.models, function(x) sqrt(ercomp(x)$sigma2))
sapply(re.models, coef)

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