gemOLGTimeCircle: Time-Circle Models (Closed Loop Overlapping Generations Models)

Description

Some examples of time-circle models, which usually contain overlapping generations. When we connect together the head and tail of time of a dynamic model, we get a time-circle model which treats time as a circle of finite length instead of a straight line of infinite length. The (discounted) output of the final period will enter the utility function and the production function of the first period, which implies the influence mechanism of the past on the present is just like the influence mechanism of the present on the future. A time-circle OLG model may be called a reincarnation model.

In a time-circle model, time can be thought of as having no beginning and no end. And we can assume all souls meet in a single market (see Shell, 1971), or there are implicit financial instruments that facilitate transactions between adjacent generations.

As in the Arrow<U+2013>Debreu approach, in the following examples with production, we assume that firms operate independently and are not owned by consumers. That is, there is no explicit property of firms as the firms do not make pure profit at equilibrium (constant return to scale) (see de la Croix and Michel, 2002, page 292).

Usage

gemOLGTimeCircle(...)

Arguments

...

arguments to be passed to the function sdm2.

References

de la Croix, David and Philippe Michel (2002, ISBN: 9780521001151) A Theory of Economic Growth: Dynamics and Policy in Overlapping Generations. Cambridge University Press.

Diamond, Peter (1965) National Debt in a Neoclassical Growth Model. American Economic Review. 55 (5): 1126-1150.

Samuelson, P. A. (1958) An Exact Consumption-Loan Model of Interest with or without the Social Contrivance of Money. Journal of Political Economy, vol. 66(6): 467-482.

Shell, Karl (1971) Notes on the Economics of Infinity. Journal of Political Economy. 79 (5): 1002<U+2013>1011.

Examples

Run this code

# NOT RUN {
#### a time-circle pure exchange economy with two-period-lived consumers
## In this exampe, each agent sells some labor to the previous generation
## and buys some labor from the next generation.

# Here n can tend to infinity.
n <- 4 # the number of periods and consumers (i.e. laborers).
lab.age1 <- 1 # labor endowment of age1
lab.age2 <- 0 # labor endowment of age2

S <- lab.age1 * diag(n) + lab.age2 * diag(n)[, c(2:n, 1)]

# Suppose each consumer has a utility function log(c1) + log(c2).
beta.consumer <- c(0.5, 0.5)
index <- c(1:n, 1)
dstl.consumer <- list()
for (k in 1:n) {
  dstl.consumer[[k]] <- node_new(
    "util",
    type = "CD", alpha = 1,
    beta = beta.consumer,
    paste0("lab", index[k]), paste0("lab", index[k + 1])
  )
}

ge <- sdm2(
  A = dstl.consumer,
  B = matrix(0, n, n, TRUE),
  S0Exg = S,
  names.commodity = paste0("lab", 1:n),
  numeraire = "lab1"
)

ge$p
ge$D
ge$S

## Introduce population growth into the above pure exchange economy.
GRExg <- 0.03 # the population growth rate

S <- S %*% diag((1 + GRExg)^(0:(n - 1)))
S[1, n] <- S[1, n] * (1 + GRExg)^-n

dstl.consumer[[n]] <- node_new(
  "util",
  type = "CD", alpha = 1,
  beta = beta.consumer,
  paste0("lab", n), "cc1"
)
node_set(dstl.consumer[[n]],
         "cc1",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "lab1"
)

ge <- sdm2(
  A = dstl.consumer,
  B = matrix(0, n, n, TRUE),
  S0Exg = S,
  names.commodity = paste0("lab", 1:n),
  numeraire = "lab1"
)

ge$p
ge$D
ge$DV
ge$S

#### a time-circle pure exchange economy with three-period-lived consumers
## Suppose each consumer has a utility function log(c1) + log(c2) + log(c3).
## See gemOLGPureExchange_2_2.
n <- 5 # the number of periods and consumers (i.e. laborers).
lab.age1 <- 1 # labor endowment of age1
lab.age2 <- 1
lab.age3 <- 0
S <- lab.age1 * diag(n) + lab.age2 * diag(n)[, c(2:n, 1)] + lab.age3 * diag(n)[, c(3:n, 1:2)]

index <- c(1:n, 1, 2)
dstl.consumer <- list()
beta.consumer <- c(1 / 3, 1 / 3, 1 / 3)
for (k in 1:n) {
  dstl.consumer[[k]] <- node_new(
    "util",
    type = "CD", alpha = 1,
    beta = beta.consumer,
    paste0("lab", index[k]), paste0("lab", index[k + 1]), paste0("lab", index[k + 2])
  )
}

ge <- sdm2(
  A = dstl.consumer,
  B = matrix(0, n, n, TRUE),
  S0Exg = S,
  names.commodity = paste0("lab", 1:n),
  numeraire = "lab1"
)

ge$p
ge$D

## Introduce population growth into the above pure exchange economy.
GRExg <- 0.03 # the population growth rate
S <- S %*% diag((1 + GRExg)^(0:(n - 1)))
S[1, n - 1] <- S[1, n - 1] * (1 + GRExg)^-n
S[1:2, n] <- S[1:2, n] * (1 + GRExg)^-n

dstl.consumer[[n - 1]] <- node_new(
  "util",
  type = "CD", alpha = 1,
  beta = beta.consumer,
  paste0("lab", n - 1), paste0("lab", n), "cc1"
)
node_set(dstl.consumer[[n - 1]], "cc1",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "lab1"
)

dstl.consumer[[n]] <- node_new(
  "util",
  type = "CD", alpha = 1,
  beta = beta.consumer,
  paste0("lab", n), "cc1", "cc2"
)
node_set(dstl.consumer[[n]], "cc1",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "lab1"
)
node_set(dstl.consumer[[n]], "cc2",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "lab2"
)

ge <- sdm2(
  A = dstl.consumer,
  B = matrix(0, n, n, TRUE),
  S0Exg = S,
  names.commodity = paste0("lab", 1:n),
  numeraire = "lab1"
)

ge$p
growth_rate(ge$p) + 1   # 1 / (1 + GRExg)
ge$D
ge$DV

## Suppose each consumer has a Leontief-type utility function min(c1, c2, c3).
discount.factor <- c(1, 1 / (1 + GRExg), 1 / (1 + GRExg)^2)
beta.consumer <- prop.table(discount.factor)

lapply(dstl.consumer, function(dst) dst$beta <- beta.consumer)

ge <- sdm2(
  A = dstl.consumer,
  B = matrix(0, n, n, TRUE),
  S0Exg = S,
  names.commodity = paste0("lab", 1:n),
  numeraire = "lab1"
)

ge$p
growth_rate(ge$p) + 1 # 1 / (1 + GRExg)
ge$D
ge$DV

#### a time-circle model with production and two-period-lived consumers
## Suppose each consumer has a utility function log(c1) + log(c2).
n <- 5 # the number of periods, consumers and firms.
S <- matrix(NA, 2 * n, 2 * n)

S.lab.consumer <- diag(1, n)
S[(n + 1):(2 * n), (n + 1):(2 * n)] <- S.lab.consumer

B <- matrix(0, 2 * n, 2 * n)
B[1:n, 1:n] <- diag(n)[, c(2:n, 1)]

dstl.firm <- list()
beta.firm <- c(1 / 3, 2 / 3)
for (k in 1:n) {
  dstl.firm[[k]] <- node_new(
    "prod",
    type = "CD", alpha = 5,
    beta = beta.firm,
    paste0("lab", k), paste0("prod", k)
  )
}

index <- c(1:n, 1)
beta.consumer <- c(1 / 2, 1 / 2)
dstl.consumer <- list()
for (k in 1:n) {
  dstl.consumer[[k]] <- node_new(
    "util",
    type = "CD", alpha = 1,
    beta = beta.consumer,
    paste0("prod", index[k]), paste0("prod", index[k + 1])
  )
}

ge <- sdm2(
  A = c(dstl.firm, dstl.consumer),
  B = B,
  S0Exg = S,
  names.commodity = c(paste0("prod", 1:n), paste0("lab", 1:n)),
  names.agent = c(paste0("firm", 1:n), paste0("consumer", 1:n)),
  numeraire = "lab1"
)

ge$p
ge$D

## Introduce population growth into the above economy.
GRExg <- 0.03 # the population growth rate

S[(n + 1):(2 * n), (n + 1):(2 * n)] <- diag((1 + GRExg)^(0:(n - 1)), n)
B[1,n] <- (1 + GRExg)^(-n)

dstl.consumer[[n]] <- node_new(
  "util",
  type = "CD", alpha = 1,
  beta = beta.consumer,
  paste0("prod", n),"cc1"
)
node_set(dstl.consumer[[n]], "cc1",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "prod1"
)

ge <- sdm2(
  A = c(dstl.firm, dstl.consumer),
  B = B,
  S0Exg = S,
  names.commodity = c(paste0("prod", 1:n), paste0("lab", 1:n)),
  names.agent = c(paste0("firm", 1:n), paste0("consumer", 1:n)),
  numeraire = "lab1",
  policy = makePolicyMeanValue(30),
  maxIteration = 1,
  numberOfPeriods = 600,
  ts = TRUE
)

ge$p
growth_rate(ge$p[1:n]) + 1   # 1 / (1 + GRExg)
growth_rate(ge$p[(n + 1):(2 * n)]) + 1   # 1 / (1 + GRExg)
ge$D
ge$DV

#### a time-circle model with production and one-period-lived consumers
## These consumers also can be regarded as infinite-lived carpe diem agents,
## who have endowments in each period, maximize the instantaneous utility and do not
## consider the future.
n <- 5 # the number of periods, consumers and firms.
GRExg <- 0.03 # the population growth rate
discount.factor.last.output <- (1 + GRExg)^(-n)
S <- matrix(NA, 2 * n, 2 * n)

S.lab.consumer <- diag((1 + GRExg)^(0:(n - 1)), n)
S[(n + 1):(2 * n), (n + 1):(2 * n)] <- S.lab.consumer

B <- matrix(0, 2 * n, 2 * n)
B[1:n, 1:n] <- diag(n)[, c(2:n, 1)]
B[1, n] <- discount.factor.last.output

beta.firm <- c(1 / 3, 2 / 3)
beta.consumer <- c(1 / 2, 1 / 2)

dstl.firm <- list()
for (k in 1:n) {
  dstl.firm[[k]] <- node_new(
    "prod",
    type = "CD", alpha = 5,
    beta = beta.firm,
    paste0("lab", k), paste0("prod", k)
  )
}

dstl.consumer <- list()
for (k in 1:n) {
  dstl.consumer[[k]] <- node_new(
    "util",
    type = "CD", alpha = 1,
    beta = beta.consumer,
    paste0("lab", k), paste0("prod", k)
  )
}

ge <- sdm2(
  A = c(dstl.firm, dstl.consumer),
  B = B,
  S0Exg = S,
  names.commodity = c(paste0("prod", 1:n), paste0("lab", 1:n)),
  names.agent = c(paste0("firm", 1:n), paste0("consumer", 1:n)),
  numeraire = "lab1",
  policy = makePolicyMeanValue(30),
  maxIteration = 1,
  numberOfPeriods = 600,
  ts = TRUE
)

ge$p
growth_rate(ge$p[1:n]) + 1   # 1 / (1 + GRExg)
growth_rate(ge$p[(n + 1):(2 * n)]) + 1   # 1 / (1 + GRExg)
ge$D

## the reduced form of the above model
dst.firm <- node_new(
  "prod",
  type = "CD", alpha = 5,
  beta = beta.firm,
  "lab", "prod"
)

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

ge2 <- 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",
  GRExg = 0.03
)

ge2$p
ge2$D

#### a time-circle model with production and three-period-lived consumers
## Suppose each consumer has a utility function log(c1) + log(c2) + log(c3).
n <- 6 # the number of periods, consumers and firms.
GRExg <- 0.03 # the population growth rate
S <- matrix(NA, 2 * n, 2 * n)

S.lab.consumer <- rbind(diag((1 + GRExg)^(0:(n - 1)), n), 0) +
  rbind(0, diag((1 + GRExg)^(0:(n - 1)), n))
S.lab.consumer <- S.lab.consumer[1:n, ]
S.lab.consumer[1, n] <- (1 + GRExg)^-1

S[(n + 1):(2 * n), (n + 1):(2 * n)] <- S.lab.consumer

B <- matrix(0, 2 * n, 2 * n)
B[1:n, 1:n] <- diag(n)[, c(2:n, 1)]
B[1, n] <- (1 + GRExg)^(-n)

beta.firm <- c(2 / 5, 3 / 5)
beta.consumer <- c(1 / 3, 1 / 3, 1 / 3)

dstl.firm <- list()
for (k in 1:n) {
  dstl.firm[[k]] <- node_new(
    "prod",
    type = "CD", alpha = 5,
    beta = beta.firm,
    paste0("lab", k), paste0("prod", k)
  )
}

dstl.consumer <- list()
for (k in 1:(n - 2)) {
  dstl.consumer[[k]] <- node_new(
    "util",
    type = "CD", alpha = 1,
    beta = beta.consumer,
    paste0("prod", k), paste0("prod", k + 1), paste0("prod", k + 2)
  )
}

dstl.consumer[[n - 1]] <- node_new(
  "util",
  type = "CD", alpha = 1,
  beta = beta.consumer,
  paste0("prod", n - 1), paste0("prod", n), "cc1"
)
node_set(dstl.consumer[[n - 1]], "cc1",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "prod1"
)

dstl.consumer[[n]] <- node_new(
  "util",
  type = "CD", alpha = 1,
  beta = beta.consumer,
  paste0("prod", n), "cc1", "cc2"
)
node_set(dstl.consumer[[n]], "cc1",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "prod1"
)
node_set(dstl.consumer[[n]], "cc2",
         type = "Leontief", a = (1 + GRExg)^(-n), # a discounting factor
         "prod2"
)

ge <- sdm2(
  A = c(dstl.firm, dstl.consumer),
  B = B,
  S0Exg = S,
  names.commodity = c(paste0("prod", 1:n), paste0("lab", 1:n)),
  names.agent = c(paste0("firm", 1:n), paste0("consumer", 1:n)),
  numeraire = "lab1",
  policy = makePolicyMeanValue(30),
  maxIteration = 1,
  numberOfPeriods = 600,
  ts = TRUE
)

ge$p
growth_rate(ge$p[1:n]) + 1   # 1 / (1 + GRExg)
growth_rate(ge$p[(n + 1):(2 * n)]) + 1   # 1 / (1 + GRExg)
ge$D
ge$DV
# }
# NOT RUN {
# }

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Description

Usage

Arguments

References

See Also

Examples