GE (version 0.3.8)

demInsufficientEffectiveDemand_3_3: A Disequilibrium Model Illustrating Insufficient Effective Demand (Supply-demand Structural Mismatch)

Description

A disequilibrium model illustrating supply-demand structural mismatch and insufficient effective demand. Assume that from the 5th period to the 30th period, the producer expects the sales rate of products to decline, so he reduces investment in production and increases the demand for value storage means (such as foreign assets, gold, etc.); the laborer expects the unemployment rate to rise, so he reduces consumption and increases the demand for value storage means.

Here the supplier of value storage means is referred to as ROW (the rest of the world).

Usage

demInsufficientEffectiveDemand_3_3(...)

Arguments

...

arguments to be passed to the function sdm2.

Examples

# \donttest{
dst.firm <- node_new("firm",
  type = "SCES", es = 1,
  alpha = 1, beta = c(1, 0),
  "production", "store of value"
)
node_set(dst.firm, "production",
  type = "SCES", alpha = 1,
  beta = c(0.5, 0.5), es = 0.5,
  "prod", "lab"
)

dst.laborer <- node_new("util",
  type = "SCES", es = 1,
  alpha = 1, beta = c(1, 0),
  "prod", "store of value"
)

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

policy.demand <- function(time, A, state) {
  if ((time >= 5 && time <= 30) || (time >= 100)) {
    A[[1]]$beta <- c(0.9, 0.1)
    A[[2]]$beta <- c(0.9, 0.1)
  }

  if ((time > 30) && (time < 100)) {
    A[[1]]$beta <- c(1, 0)
    A[[2]]$beta <- c(1, 0)
  }

  state
}

ge <- sdm2(
  A = list(dst.firm, dst.laborer, dst.ROW),
  B = matrix(c(
    1, 0, 0,
    0, 0, 0,
    0, 0, 0
  ), 3, 3, TRUE),
  S0Exg = matrix(c(
    NA, NA, NA,
    NA, 100, NA,
    NA, NA, 10
  ), 3, 3, TRUE),
  names.commodity = c("prod", "lab", "store of value"),
  names.agent = c("firm", "laborer", "ROW"),
  ts = TRUE,
  policy = policy.demand,
  numberOfPeriods = 50,
  maxIteration = 1,
  numeraire = "store of value",
  z0 = c(200, 0, 0),
  depreciationCoef = 1,
  priceAdjustmentVelocity = 0.05
)

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

#### the relationship between the proportion of demand for value
## storage means and the sales rate under a fixed price vector.
p <- c(1, 1, 1)
result <- c()
for (xi in seq(0, 1, 0.01)) {
  A <- matrix(c(
    (1 - xi) * 0.5, 1 - xi, 1 - xi,
    (1 - xi) * 0.5, 0, 0,
    xi, xi, xi
  ), 3, 3, TRUE)

  # Assume that only the producer has pessimistic expectations.
  # A <- matrix(c(
  #   (1 - xi) * 0.5, 1, 1,
  #   (1 - xi) * 0.5, 0, 0,
  #   xi, 0, 0
  # ), 3, 3, TRUE)
  #
  # Assume that only the laborer has pessimistic expectations.
  # A <- matrix(c(
  #   0.5, 1 - xi, 1 - xi,
  #   0.5, 0, 0,
  #   0, xi, xi
  # ), 3, 3, TRUE)


  S <- matrix(c(
    200, 0, 0,
    0, 100, 0,
    0, 0, 10
  ), 3, 3, TRUE)

  tmp <- F_Z(A, p, S)
  result <- rbind(result, c(xi, tmp$q[1:2]))
}

matplot(result[, 1], result[, 2:3],
  type = "o", pch = 20,
  xlab = "proportion of demand for value storage means", ylab = "sales rates of prod and lab"
)
# }