Simulated data for college admissions problem

Usage

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

Description

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.

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