simsart {CDatanet}  R Documentation 
Simulating data from Tobit models with social interactions
Description
simsart
simulates censored data with social interactions (see Xu and Lee, 2015).
Usage
simsart(formula, Glist, theta, tol = 1e15, maxit = 500, cinfo = TRUE, data)
Arguments
formula 
a class object formula: a symbolic description of the model. 
Glist 
The network matrix. For networks consisting of multiple subnets, 
theta 
a vector defining the true value of 
tol 
the tolerance value used in the fixed point iteration method to compute 
maxit 
the maximal number of iterations in the fixed point iteration method. 
cinfo 
a Boolean indicating whether information is complete ( 
data 
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables
in the model. If not found in data, the variables are taken from 
Details
For a complete information model, the outcome y_i
is defined as:
\begin{cases}y_i^{\ast} = \lambda \bar{y}_i + \mathbf{z}_i'\Gamma + \epsilon_i, \\ y_i = \max(0, y_i^{\ast}),\end{cases}
where \bar{y}_i
is the average of y
among peers,
\mathbf{z}_i
is a vector of control variables,
and \epsilon_i \sim N(0, \sigma^2)
.
In the case of incomplete information modelswith rational expectations, y_i
is defined as:
\begin{cases}y_i^{\ast} = \lambda E(\bar{y}_i) + \mathbf{z}_i'\Gamma + \epsilon_i, \\ y_i = \max(0, y_i^{\ast}).\end{cases}
Value
A list consisting of:
yst 

y 
the observed censored variable. 
Ey 

Gy 
the average of y among friends. 
GEy 
the average of 
meff 
a list includinh average and individual marginal effects. 
iteration 
number of iterations performed by subnetwork in the Fixed Point Iteration Method. 
References
Xu, X., & Lee, L. F. (2015). Maximum likelihood estimation of a spatial autoregressive Tobit model. Journal of Econometrics, 188(1), 264280, doi:10.1016/j.jeconom.2015.05.004.
See Also
Examples
# Groups' size
set.seed(123)
M < 5 # Number of subgroups
nvec < round(runif(M, 100, 200))
n < sum(nvec)
# Parameters
lambda < 0.4
Gamma < c(2, 1.9, 0.8, 1.5, 1.2)
sigma < 1.5
theta < c(lambda, Gamma, sigma)
# X
X < cbind(rnorm(n, 1, 1), rexp(n, 0.4))
# Network
G < list()
for (m in 1:M) {
nm < nvec[m]
Gm < matrix(0, nm, nm)
max_d < 30
for (i in 1:nm) {
tmp < sample((1:nm)[i], sample(0:max_d, 1))
Gm[i, tmp] < 1
}
rs < rowSums(Gm); rs[rs == 0] < 1
Gm < Gm/rs
G[[m]] < Gm
}
# Data
data < data.frame(X, peer.avg(G, cbind(x1 = X[,1], x2 = X[,2])))
colnames(data) < c("x1", "x2", "gx1", "gx2")
## Complete information game
ytmp < simsart(formula = ~ x1 + x2 + gx1 + gx2, Glist = G, theta = theta,
data = data, cinfo = TRUE)
data$yc < ytmp$y
## Incomplete information game
ytmp < simsart(formula = ~ x1 + x2 + gx1 + gx2, Glist = G, theta = theta,
data = data, cinfo = FALSE)
data$yi < ytmp$y