GE (version 0.1.1)

demand_coefficient: Compute Demand Coefficients of an Agent with a Demand Structural Tree

Description

Given a price vector, this function computes the demand coefficients of an agent with a demand structural tree. The class of a demand structural tree is Node defined by the package data.tree.

Usage

demand_coefficient(node, p)

Arguments

node

the demand structural tree.

p

the price vector with names of commodities.

Value

A vector consisting of demand coefficients.

Details

The demand for various commodities by an economic agent can be expressed by a demand structure tree. Each non-leaf node can be regarded as the output of all its child nodes. Each node can be regarded as an input of its parent node. In other words, the commodity represented by each non-leaf node is a composite commodity composed of the commodities represented by its child nodes. Each non-leaf node usually has an attribute named type. This attribute describes the input-output relationship between the child nodes and the parent node. This relationship can sometimes be represented by a production function or a utility function. The type attribute of each non-leaf node can take the following values.

• SCES. SCES can also be written as CES. In this case, this node also has parameters alpha, beta and es (or sigma = 1 - 1 / es). alpha and es are scalars. beta is a vector. These parameters are parameters of a standard CES function (see SCES and SCES_A).

• Leontief. In this case, this node also has the parameter a, which is a vector and is the parameter of a Leontief function.

• CD. CD is Cobb-Douglas. In this case, this node also has parameters alpha and beta. These parameters are parameters of a Cobb-Douglas function.

• FIN. That is the financial type. FIN can also be written as money, dividends, bonds and taxes. In this case, this node also has the parameter rate or beta. If the parameter beta is not NULL, then the parameter rate will be ignored. The parameter rate applies to all situations, while the parameter beta only applies for some special cases. For FIN nodes, the first child node should represent for a physical commodity or a composite commodity containing a physical commodity, and other child nodes represent for financial instruments. The parameter beta indicates the proportion of each child node's expenditure. The parameter rate indicates the expenditure ratios between financial-instrument-type child nodes and the first child node. The first element of the parameter rate indicates the amount of the first child node needed to get a unit of output.

• FUNC. That is the function type. In this case, this node also has an attribute named func. The value of that attribute is a function which calculates the demand coefficient for the child nodes. The argument of that function is a price vector. The length of that price vector is equal to the number of the child nodes.

Examples

# NOT RUN {
#### a Leontief-type node
dst <- node_new("firm",
  type = "Leontief", a = c(0.5, 0.1),
  "wheat", "iron"
)
print(dst, "type")
node_print(dst)
plot(dst)
node_plot(dst)

demand_coefficient(dst, p = c(wheat = 1, iron = 2)) # the same as a = c(0.5, 0.1)

#### a CD-type node
dst <- node_new("firm",
  type = "CD", alpha = 1, beta = c(0.5, 0.5),
  "wheat", "iron"
)

demand_coefficient(dst, p = c(wheat = 1, iron = 2))
# the same as the following
CD_A(1, c(0.5, 0.5), c(1, 2))

#### a SCES-type node
dst <- node_new("firm",
  type = "SCES",
  alpha = 2, beta = c(0.8, 0.2), es = 0.5,
  "wheat", "iron"
)

demand_coefficient(dst, p = c(wheat = 1, iron = 2))

# the same as the following
SCES_A(alpha = 2, Beta = c(0.8, 0.2), p = c(1, 2), es = 0.5)
CES_A(sigma = 1 - 1 / 0.5, alpha = 2, Beta = c(0.8, 0.2), p = c(1, 2), Theta = c(0.8, 0.2))

#### a func-type node
dst <- node_new("firm",
  type = "FUNC",
  func = function(p) {
    CES_A(
      sigma = -1, alpha = 2,
      Beta = c(0.8, 0.2), p,
      Theta = c(0.8, 0.2)
    )
  },
  "wheat", "iron"
)

demand_coefficient(dst, p = c(wheat = 1, iron = 2))

# the same as the following
CES_A(sigma = -1, alpha = 2, Beta = c(0.8, 0.2), p = c(1, 2), Theta = c(0.8, 0.2))

####
p <- c(wheat = 1, iron = 3, labor = 2, capital = 4)

dst <- node_new("firm 1",
  type = "SCES", sigma = -1, alpha = 1, beta = c(1, 1),
  "cc1", "cc2"
)
node_set(dst, "cc1",
  type = "Leontief", a = c(0.6, 0.4),
  "wheat", "iron"
)
node_set(dst, "cc2",
  type = "SCES", sigma = -1, alpha = 1, beta = c(1, 1),
  "labor", "capital"
)

node_plot(dst)
demand_coefficient(dst, p)

####
p <- c(product = 1, labor = 1, money = 1)
dst <- node_new("firm",
  type = "FIN", rate = c(0.75, 1 / 3),
  "cc1", "money"
) # a financial-type node
node_set(dst, "cc1",
  type = "Leontief", a = c(0.8, 0.2),
  "product", "labor"
)

node_plot(dst)
demand_coefficient(dst, p)

#### the same as above
p <- c(product = 1, labor = 1, money = 1)
dst <- node_new("firm",
  type = "Leontief", a = c(0.8, 0.2),
  "cc1", "cc2"
)
node_set(dst, "cc1",
  type = "FIN", rate = c(0.75, 1 / 3),
  "product", "money"
)

node_set(dst, "cc2",
  type = "FIN", rate = c(0.75, 1 / 3),
  "labor", "money"
)
node_plot(dst)
demand_coefficient(dst, p)

#### the same as above
p <- c(product = 1, labor = 1, money = 1)
dst <- node_new("firm",
  type = "FIN", rate = c(1, 1 / 3),
  "cc1", "money"
) # Financial-type Demand Structure
node_set(dst, "cc1",
  type = "Leontief", a = c(0.6, 0.15),
  "product", "labor"
)

node_plot(dst)
demand_coefficient(dst, p)
# }