GE (version 0.5.0)

gemstEndogenousProductionFunction_2_2: A General Equilibrium Model with Endogenous Production Function

Description

These are examples of spot-trading dynamic equilibria with endogenous production functions. In these models, the parameters of the production functions vary with the output level in the previous period.

To address locally or globally increasing returns to scale, we can use an endogenous CES-type production function with constant returns to scale, instead of adopting a more complex functional form.

Usage

gemstEndogenousProductionFunction_2_2(...)

Arguments

...

arguments to be passed to the function sdm2.

See Also

gemstIntertemporal_EndogenousProductionFunction_2_2

Examples

# \donttest{
dst.firm <- node_new(
  "output",
  type = "CD", alpha = NA, beta = c(0.5, 0.5),
  "prod", "lab"
)

dst.consumer <- node_new(
  "util",
  type = "Leontief", a = 1,
  "prod"
)

ge <- sdm2(
  A = list(dst.firm, dst.consumer),
  B = matrix(c(
    1, 0,
    0, 0
  ), 2, 2, TRUE),
  S0Exg = matrix(c(
    NA, NA,
    NA, 1
  ), 2, 2, TRUE),
  names.commodity = c("prod", "lab"),
  names.agent = c("firm", "consumer"),
  numeraire = "lab",
  z0 = c(1, 1),
  p0 = c(1, 1),
  ts = TRUE,
  policy = list(
    function(A, state) {
      A[[1]]$alpha <- 5 * state$last.z[1]^0.1
    },
    policyMarketClearingPrice
  ),
  numberOfPeriods = 40,
  maxIteration = 1
)

matplot(ge$ts.z, type = "o", pch = 20)

# #### An example of a spot-trading dynamic equilibrium with corrective taxation.
# tau <- 0.2
#
# dst.firm <- node_new(
#   "output",
#   type = "CD", alpha = NA, beta = c(0.5, 0.5),
#   "prod", "lab"
# )
#
# dst.consumer <- node_new(
#   "util",
#   type = "Leontief", a = 1,
#   "prod"
# )
#
# ge <- sdm2(
#   A = list(dst.firm, dst.consumer),
#   B = matrix(c(
#     1, 0,
#     0, 0
#   ), 2, 2, TRUE),
#   S0Exg = matrix(c(
#     NA, NA,
#     100 * tau, 100 * (1 - tau)
#   ), 2, 2, TRUE),
#   names.commodity = c("prod", "lab"),
#   names.agent = c("firm", "consumer"),
#   numeraire = "lab",
#   z0 = c(100, 100),
#   p0 = c(1, 1),
#   ts = TRUE,
#   policy = list(
#     function(A, state) {
#       A[[1]]$alpha <- 2 * state$last.z[1]^0.1
#     },
#     policyMarketClearingPrice
#   ),
#   numberOfPeriods = 40,
#   maxIteration = 1
# )
#
# matplot(ge$ts.z, type = "o", pch = 20)
# ge$z
# }