mspe.plot {SCtools}

Plot the post/pre-treatment MSPE ratio

Description

Plots the post/pre-treatment mean square prediction error ratio for the treated unit and placebos.

Usage

mspe.plot(
  tdf,
  discard.extreme = FALSE,
  mspe.limit = 20,
  plot.hist = FALSE,
  title = NULL,
  xlab = "Post/Pre MSPE ratio",
  ylab = NULL
)

mspe_plot(
  tdf,
  discard.extreme = FALSE,
  mspe.limit = 20,
  plot.hist = FALSE,
  title = NULL,
  xlab = "Post/Pre MSPE ratio",
  ylab = NULL
)

Arguments

tdf

An object constructed by generate.placebos.

discard.extreme

Logical. Whether or not placebos with high pre-treatement MSPE should be excluded from the plot.

mspe.limit

Numerical. Used if discard.extreme is TRUE. It indicates how many times the pretreatment MSPE of a placebo should be higher than that of the treated unit to be considered extreme and discarded. Default is 20.

plot.hist

Logical. If FALSE, a dotplot with each unit name and its post/pre treatment MSPE ratio is produced. If TRUE, a histogram is produced, with the frequency of each ratio. Should be set to TRUE when there are many controls, to make visualization easier.

title

Character. Optional. Title of the plot.

xlab

Character. Optional. Label of the x axis.

ylab

Character. Optional. Label of the y axis.

Details

Post/pre-treatement mean square prediction error ratio is the difference between the observed outcome of a unit and its synthetic control, before and after treatement. A higher ratio means a small pretreatment prediction error (a good synthetic control), and a high post-treatment MSPE, meaning a large difference between the unit and its synthetic control after the intervention. By calculating this ratio for all placebos, the test can be interpreted as looking at how likely the result obtained for a single treated case with a synthetic control analysis could have occurred by chance given no treatement. For more detailed description, see Abadie, Diamond, and Hainmueller (2011, 2014).

Value

p.dot

Plot with the post/pre MSPE ratios for the treated unit and each placebo indicated individually. Returned if plot.hist is FALSE.

p.dens

Histogram of the distribution of post/pre MSPE ratios for all placebos and the treated unit. Returned if plot.hist is TRUE.

References

Abadie, A., Diamond, A., Hainmueller, J. (2014). Comparative Politics and the Synthetic Control Method. American Journal of Political Science Forthcoming 2014.

Synthetic : An R Package for Synthetic Control Methods in Comparative Case Studies. Journal of Statistical Software 42 (13) 1–17.

Abadie, A., Diamond, A., Hainmueller, J. (2011). Synth: An R Package for Synthetic Control Methods in Comparative Case Studies. Journal of Statistical Software 42 (13) 1–17.

Abadie A, Diamond A, Hainmueller J (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association 105 (490) 493–505.

See Also

generate.placebos, mspe.test, plot_placebos, synth

Examples

## Example with toy data from 'Synth'
library(Synth)
# Load the simulated data
data(synth.data)

# Execute dataprep to produce the necessary matrices for 'Synth'
dataprep.out<-
  dataprep(
    foo = synth.data,
    predictors = c("X1"),
    predictors.op = "mean",
    dependent = "Y",
    unit.variable = "unit.num",
    time.variable = "year",
    special.predictors = list(
      list("Y", 1991, "mean")
    ),
    treatment.identifier = 7,
    controls.identifier = c(29, 2, 13, 17),
    time.predictors.prior = c(1984:1989),
    time.optimize.ssr = c(1984:1990),
    unit.names.variable = "name",
    time.plot = 1984:1996
)

# run the synth command to create the synthetic control
synth.out <- synth(dataprep.out, Sigf.ipop=2)

## run the generate.placebos command to reassign treatment status
## to each unit listed as control, one at a time, and generate their
## synthetic versions. Sigf.ipop = 2 for faster computing time. 
## Increase to the default of 5 for better estimates. 
tdf <- generate.placebos(dataprep.out,synth.out, Sigf.ipop = 2)

## Test how extreme was the observed treatment effect given the placebos:
ratio <- mspe.test(tdf)
ratio$p.val

mspe.plot(tdf, discard.extreme = FALSE)