Results of Monte Carlo Simulations in Klein (2015b)

Results of Monte Carlo Simulations in Klein (2015b) for 40 two-group markets.

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)
       })

# NOT RUN {
## 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)
# }