klein15b {matchingMarkets} | R Documentation |
Results of Monte Carlo Simulations in Klein (2015b)
Description
Results of Monte Carlo Simulations in Klein (2015b) for 40 two-group markets.
Usage
data(klein15b)
Format
A list containing the following elements:
- exp.5.5.ols
Benchmark study, OLS: coefficient estimates for 40 markets with groups of 5. Data for all 5 group members is observed.
- exp.5.5.ntu
Benchmark study, structural model.
- exp.6.5.ols
Experiment 1, OLS: coefficient estimates for 40 markets with groups of 6. Only Data for 5 group members is observed.
- exp.6.5.ntu
Experiment 1, structural model.
- exp.6.6.ols
Experiment 2, OLS: coefficient estimates for 40 markets with groups of 6. Data for all 6 group members is observed but only a random sample of 250 of the 922 counterfactual groups is used in the analysis.
- exp.6.6.ntu
Experiment 2, structural model.
References
Klein, T. (2015a). Does Anti-Diversification Pay? A One-Sided Matching Model of Microcredit. Cambridge Working Papers in Economics, #1521.
Klein, T. (2015b). Analysis of stable matchings in R: Package matchingMarkets. Vignette to R package matchingMarkets, The Comprehensive R Archive Network.
Examples
## Plot of posterior distributions
data(klein15b)
tpe <- c(rep("Benchmark",2), rep("Experiment 1",2), rep("Experiment 2",2))
for(i in seq(1,length(klein15b)-1,2)){
ntu <- klein15b[[i]]
ols <- klein15b[[i+1]]
ntu <- ntu[,colnames(ntu) == "beta.wst.ieq"]
ols <- ols[,colnames(ols) == "beta.wst.ieq"]
if(i == 1){
draws <- data.frame(Structural=ntu, OLS=ols, type=tpe[i]) #, stringsAsFactors=FALSE
} else{
draws <- rbind(draws, data.frame(Structural=ntu, OLS=ols, type=tpe[i]))
}
}
library(lattice)
lattice.options(default.theme = standard.theme(color = FALSE))
keys <- list(text=c("Structural model","OLS"), space="top", columns=2, lines=TRUE)
densityplot( ~ Structural + OLS | type, plot.points=FALSE, auto.key=keys,
data = draws, xlab = "coefficient draws", ylab = "density", type = "l",
panel = function(x,...) {
panel.densityplot(x,...)
panel.abline(v=-1, lty=3)
})
## Modes of posterior distributions
## load data
data(klein15b)
## define function to obtain the mode
mode <- function(x){
d <- density(x,bw="SJ")
formatC(round(d$x[which.max(d$y)], 3), format='f', digits=3)
}
## Benchmark study
apply(klein15b$exp.5.5.ntu, 2, mode)
apply(klein15b$exp.5.5.ols, 2, mode)
## Experiment 1
apply(klein15b$exp.6.5.ntu, 2, mode)
apply(klein15b$exp.6.5.ols, 2, mode)
## Experiment 2
apply(klein15b$exp.6.6.ntu, 2, mode)
apply(klein15b$exp.6.6.ols, 2, mode)