gemInputOutputTable_5_4: A General Equilibrium Model based on a 5×4 Input-Output Table...

In GE: General Equilibrium Modeling

A General Equilibrium Model based on a 5×4 Input-Output Table (see Zhang Xin, 2017, Table 8.6.1)

Description

This is a general equilibrium model based on a 5×4 input-output table (see Zhang Xin, 2017, Table 8.6.1).

Usage

gemInputOutputTable_5_4(
  dstl,
  supply.labor = 850,
  supply.capital = 770,
  names.commodity = c("agri", "manu", "serv", "lab", "cap"),
  names.agent = c("agri", "manu", "serv", "hh")
)

Arguments

dstl	a demand structure tree list.
supply.labor	the supply of labor.
supply.capital	the supply of capital.
names.commodity	names of commodities.
names.agent	names of agents.

Details

Given a 5×4 input-output table (e.g., see Zhang Xin, 2017, Table 8.6.1), this model calculates the corresponding general equilibrium. This input-output table contains 3 production sectors and one household. The household consumes products and supplies labor and capital.

Value

A general equilibrium which is a list with the following elements:

• D - the demand matrix, also called the input table. Wherein the benchmark prices are used.

• DV - the demand value matrix, also called the value input table. Wherein the current price is used.

• SV - the supply value matrix, also called the value output table. Wherein the current price is used.

• ... - some elements returned by the CGE::sdm function

References

Zhang Xin (2017, ISBN: 9787543227637) Principles of Computable General Equilibrium Modeling and Programming (Second Edition). Shanghai: Gezhi Press. (In Chinese)

Examples

es.agri <- 0.2 # the elasticity of substitution
es.manu <- 0.3
es.serv <- 0.1

es.VA.agri <- 0.25
es.VA.manu <- 0.5
es.VA.serv <- 0.8

d.agri <- c(260, 345, 400, 200, 160)
d.manu <- c(320, 390, 365, 250, 400)
d.serv <- c(150, 390, 320, 400, 210)
d.hh <- c(635, 600, 385, 0, 0)
# d.hh <- c(635, 600, 100, 0, 0)

IT <- cbind(d.agri, d.manu, d.serv, d.hh)
OT <- matrix(c(
  1365, 0, 0, 0,
  0, 1725, 0, 0,
  0, 0, 1470, 0,
  0, 0, 0, 850,
  0, 0, 0, 770
), 5, 4, TRUE)

dimnames(IT) <- dimnames(OT) <-
  list(
    c("agri", "manu", "serv", "lab", "cap"),
    c("agri", "manu", "serv", "hh")
  )

addmargins(IT)
addmargins(OT)

dst.agri <- node_new("sector.agri",
                     type = "SCES", es = es.agri,
                     alpha = 1,
                     beta = prop.table(
                       c(sum(d.agri[1:3]), sum(d.agri[4:5]))
                     ),
                     "cc1.agri", "cc2.agri"
)
node_set(dst.agri, "cc1.agri",
         type = "Leontief",
         a = prop.table(d.agri[1:3]),
         "agri", "manu", "serv"
)
node_set(dst.agri, "cc2.agri",
         type = "SCES", es = es.VA.agri,
         alpha = 1,
         beta = prop.table(d.agri[4:5]),
         "lab", "cap"
)


dst.manu <- node_new("sector.manu",
                     type = "SCES", es = es.manu,
                     alpha = 1,
                     beta = prop.table(
                       c(sum(d.manu[1:3]), sum(d.manu[4:5]))
                     ),
                     "cc1.manu", "cc2.manu"
)
node_set(dst.manu, "cc1.manu",
         type = "Leontief",
         a = prop.table(d.manu[1:3]),
         "agri", "manu", "serv"
)
node_set(dst.manu, "cc2.manu",
         type = "SCES", es = es.VA.manu,
         alpha = 1,
         beta = prop.table(d.manu[4:5]),
         "lab", "cap"
)

dst.serv <- node_new("sector.serv",
                     type = "SCES", es = es.serv,
                     alpha = 1,
                     beta = prop.table(
                       c(sum(d.serv[1:3]), sum(d.serv[4:5]))
                     ),
                     "cc1.serv", "cc2.serv"
)
node_set(dst.serv, "cc1.serv",
         type = "Leontief",
         a = prop.table(d.serv[1:3]),
         "agri", "manu", "serv"
)
node_set(dst.serv, "cc2.serv",
         type = "SCES", es = es.VA.serv,
         alpha = 1,
         beta = prop.table(d.serv[4:5]),
         "lab", "cap"
)

##
dst.hh <- node_new("sector.hh",
                   type = "SCES", es = 0.5,
                   alpha = 1,
                   beta = prop.table(d.hh[1:3]),
                   "agri", "manu", "serv"
)

dstl <- list(dst.agri, dst.manu, dst.serv, dst.hh)

ge <- gemInputOutputTable_5_4(dstl)

#### labor supply increase
geLSI <- gemInputOutputTable_5_4(dstl, supply.labor = 850 * 1.08)
geLSI$p
geLSI$z / ge$z

## capital supply change
ge.CSC <- sdm2(
  A = dstl,
  B = matrix(c(
    1, 0, 0, 0,
    0, 1, 0, 0,
    0, 0, 1, 0,
    0, 0, 0, 1,
    0, 0, 0, 1
  ), 5, 4, TRUE),
  S0Exg = {
    tmp <- matrix(NA, 5, 4)
    tmp[4, 4] <- 850
    tmp[5, 4] <- 770
    tmp
  },
  names.commodity = c("agri", "manu", "serv", "lab", "cap"),
  names.agent = c("agri", "manu", "serv", "hh"),
  numeraire = "lab",
  ts = TRUE,
  numberOfPeriods = 100,
  maxIteration = 1,
  z0 = c(1365, 1725, 1470, 1620),
  p0 = rep(1, 5),
  policy = function(time, state) {
    if (time >= 5) {
      state$S[5, 4] <- 880
    }
    state
  }
)

matplot(ge.CSC$ts.p, type = "l")
matplot(ge.CSC$ts.z, type = "l")

## economic fluctuation: a sticky-price path
de <- sdm2(
  A = dstl,
  B = matrix(c(
    1, 0, 0, 0,
    0, 1, 0, 0,
    0, 0, 1, 0,
    0, 0, 0, 1,
    0, 0, 0, 1
  ), 5, 4, TRUE),
  S0Exg = {
    tmp <- matrix(NA, 5, 4)
    tmp[4, 4] <- 850
    tmp[5, 4] <- 770
    tmp
  },
  names.commodity = c("agri", "manu", "serv", "lab", "cap"),
  names.agent = c("agri", "manu", "serv", "hh"),
  numeraire = "lab",
  ts = TRUE,
  numberOfPeriods = 50,
  maxIteration = 1,
  z0 = c(1365, 1725, 1470, 1620),
  p0 = rep(1, 5),
  policy = list(
    function(time, state) {
      if (time >= 5) {
        state$S[5, 4] <- 880
      }
      state
    },
    makePolicyStickyPrice(0.5)
  ),
  priceAdjustmentVelocity = 0
)

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

GE documentation built on May 29, 2024, 2:52 a.m.