data("SIC33")

data("SIC33", package = "AER")

## Example 6.2 in Greene (2003)
## Translog model
fm_tl <- lm(output ~ labor + capital + I(0.5 * labor^2) + I(0.5 * capital^2) + I(labor * capital),
  data = log(SIC33))
## Cobb-Douglas model
fm_cb <- lm(output ~ labor + capital, data = log(SIC33))

## Table 6.2 in Greene (2003)
deviance(fm_tl)
deviance(fm_cb)
summary(fm_tl)
summary(fm_cb)
vcov(fm_tl)
vcov(fm_cb)

## Cobb-Douglas vs. Translog model
anova(fm_cb, fm_tl)
## hypothesis of constant returns
linearHypothesis(fm_cb, "labor + capital = 1")

## 3D Visualization
library("scatterplot3d")
  s3d <- scatterplot3d(log(SIC33)[,c(2, 3, 1)], pch = 16)
  s3d$plane3d(fm_cb, lty.box = "solid", col = 4)

## Interactive 3D Visualization

if(require("rgl")) {
  x <- log(SIC33)[,2]
  y <- log(SIC33)[,3]
  z <- log(SIC33)[,1]
  rgl.open()
  rgl.bbox()
  rgl.spheres(x, y, z, radius = 0.15)
  x <- seq(4.5, 7.5, by = 0.5)
  y <- seq(5.5, 10, by = 0.5)
  z <- outer(x, y, function(x, y) predict(fm_cb, data.frame(labor = x, capital = y)))
  rgl.surface(x, y, z, color = "blue", alpha = 0.5, shininess = 128)
}