gemInstantaneousEquilibriumPath_StickyDecisions {GE}

Some Examples of Instantaneous Equilibrium Paths with Sticky Decisions

Description

Some examples of instantaneous equilibrium paths with sticky decisions of a firm, that is, the firm sluggishly adjusts its technology in response to price changes.

Under the assumption of (complete) rationality, economic agents will make decisions that are most beneficial to them based on the information they have. If the information does not change, then the decision will not change. However, under the assumption of bounded rationality, the decisions made by economic agents may not be optimal. They may follow some simple rules-of-thumb, and might adjust their previous decisions sluggishly according to the changes in information, even though they have the capability to adjust flexibly, so that the new decisions are better than the old ones under the new information. Hence the current decision is not necessarily the optimal decision. Even if the information does not change, it is still possible for agents to make further improvements to this decision in the next period. It can also be said that in this case, the decision maker's decision is sticky, that is, it only makes limited improvements to the previous decision based on new information, rather than directly adjusting to the optimal decision.

Usage

gemInstantaneousEquilibriumPath_StickyDecisions(...)

Arguments

...

arguments to be passed to the function sdm2.

See Also

policyMarketClearingPrice

Examples

f <- function(stickiness.firm = 0) {
  dst.firm <- node_new("output",
    type = "Leontief", a = c(1 - stickiness.firm, stickiness.firm),
    "cc1", "cc2"
  )
  node_set(dst.firm, "cc1",
    type = "CD", alpha = 5,
    beta = c(0.5, 0.5),
    "prod", "lab"
  )
  node_set(dst.firm, "cc2",
    type = "CD", alpha = 5,
    beta = c(0.5, 0.5),
    "prod", "lab"
  )

  dst.consumer <- node_new("utility",
    type = "CD", alpha = 1,
    beta = c(0.5, 0.5),
    "prod", "lab"
  )

  ge <- sdm2(
    A = list(dst.firm, dst.consumer),
    B = diag(c(1, 0)),
    S0Exg = {
      S0Exg <- matrix(NA, 2, 2)
      S0Exg[2, 2] <- 100
      S0Exg
    },
    names.commodity = c("prod", "lab"),
    names.agent = c("firm", "consumer"),
    numeraire = "lab",
    maxIteration = 1,
    numberOfPeriods = 20,
    policy = list(
      function(time, A, state) {
        if (time > 1) {
          node_set(A[[1]], "cc2",
            type = "Leontief", a = state$last.A[, 1]
          )
        }
      },
      policyMarketClearingPrice
    ),
    ts = TRUE
  )

  print(ge$p)
  print(ge$z)
  par(mfrow = c(1, 2))
  matplot(ge$ts.p, type = "l")
  matplot(ge$ts.z, type = "l")
}

f()
f(stickiness.firm = 0.8)