Simulated data for college admissions problem

Simulate data for two-sided matching markets. In the simulation for the Sorensen (2007) model with one selection equation, an equal sharing rule of \(\lambda = 0.5\) is used.

stabsim2(m, nStudents, nColleges = length(nSlots), nSlots, colleges,
  students, outcome, selection, binary = FALSE, seed = 123,
  verbose = TRUE)

Arguments

m	integer indicating the number of markets to be simulated.
nStudents	integer indicating the number of students per market.
nColleges	integer indicating the number of colleges per market.
nSlots	vector of length nColleges indicating the number of places at each college, i.e. the college's quota.
colleges	character vector of variable names for college characteristics. These variables carry the same value for any college.
students	character vector of variable names for student characteristics. These variables carry the same value for any student.
outcome	formula for match outcomes.
selection	formula for match valuations.
binary	logical: if TRUE outcome variable is binary; if FALSE outcome variable is continuous.
seed	integer setting the state for random number generation. Defaults to set.seed(123).
verbose	.

Value

stabsim2 returns a list with the following items.

OUT

SEL

SELc

SELs

Examples

## Simulate two-sided matching data for 2 markets (m=2) with 10 students
## (nStudents=10) per market and 3 colleges (nColleges=3) with quotas of
## 2, 3, and 5 students, respectively.

xdata <- stabsim2(m=2, nStudents=10, nSlots=c(2,3,5), verbose=FALSE,
  colleges = "c1", students = "s1",
  outcome = ~ c1:s1 + eta + nu,
  selection = ~ -1 + c1:s1 + eta
)
#> Generating data for 2 matching markets... 
head(xdata$OUT)
#>   m.id         y (Intercept)        eta          nu       c1:s1         c1
#> 1    1 2.6738032           1  0.7013559  0.77996512  0.19248215 -0.3963161
#> 2    1 0.6526449           1 -0.2179749 -0.02854676 -0.10083344 -0.3963161
#> 3    1 1.8835812           1  0.1533731  0.58461375  0.14559431 -0.1627601
#> 4    1 2.5210472           1  1.2538149  0.21594157  0.05129068 -0.1627601
#> 5    1 1.5157940           1  0.8951257 -0.33320738 -0.04612428 -0.1627601
#> 6    1 0.9145091           1  0.8781335 -1.01857538  0.05495101  1.1021732
#>            s1 c.id s.id
#> 1 -0.48567831    1    6
#> 2  0.25442680    1    9
#> 3 -0.89453338    2    5
#> 4 -0.31513060    2    7
#> 5  0.28338821    2   10
#> 6  0.04985696    3    1
head(xdata$SEL)
#>   m.id          V        eta       c1:s1         c1          s1 c.id s.id D
#> 1    1  0.8938380  0.7013559  0.19248215 -0.3963161 -0.48567831    1    6 1
#> 2    1 -0.3188084 -0.2179749 -0.10083344 -0.3963161  0.25442680    1    9 1
#> 3    1  0.2989674  0.1533731  0.14559431 -0.1627601 -0.89453338    2    5 1
#> 4    1  1.3051056  1.2538149  0.05129068 -0.1627601 -0.31513060    2    7 1
#> 5    1  0.8490014  0.8951257 -0.04612428 -0.1627601  0.28338821    2   10 1
#> 6    1  0.9330845  0.8781335  0.05495101  1.1021732  0.04985696    3    1 1