knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 7, fig.align = "center")
In order to demonstrate SCtools
it is useful to start with a replication of the cannonical Basque study from Abadie and Gardeazabal (2003).
library(SCtools) library(Synth)
Now we can load the basque
data set.
data("basque")
As per the normal Synth
workflow, we need to reformat our data using the dataprep
function in which we specify our counterfactuals and our response variables.
dataprep.out <- dataprep( foo = basque, predictors = c("school.illit", "school.prim", "school.med", "school.high", "school.post.high", "invest"), predictors.op = "mean", time.predictors.prior = 1964:1969, special.predictors = list( list("gdpcap", 1960:1969 ,"mean"), list("sec.agriculture", seq(1961, 1969, 2), "mean"), list("sec.energy", seq(1961, 1969, 2), "mean"), list("sec.industry", seq(1961, 1969, 2), "mean"), list("sec.construction", seq(1961, 1969, 2), "mean"), list("sec.services.venta", seq(1961, 1969, 2), "mean"), list("sec.services.nonventa", seq(1961, 1969, 2), "mean"), list("popdens", 1969, "mean")), dependent = "gdpcap", unit.variable = "regionno", unit.names.variable = "regionname", time.variable = "year", treatment.identifier = 17, controls.identifier = c(2:16, 18), time.optimize.ssr = 1960:1969, time.plot = 1955:1997)
Now, we can run the SCM algorithm using the synth
function.
synth.out <- readRDS("synth_out.rds")
synth.out <- synth(data.prep.obj = dataprep.out, method = "BFGS")
Synth
provides some additional helper functions to extract information from the outputted object including the ability to analyze the outputs:
gaps <- dataprep.out$Y1plot - (dataprep.out$Y0plot %*% synth.out$solution.w)
And generate plots of the counterfactual:
path.plot(synth.res = synth.out, dataprep.res = dataprep.out, Ylab = "real per-capita GDP (1986 USD, thousand)", Xlab = "year", Ylim = c(0, 12), Legend = c("Basque country", "synthetic Basque country"), Legend.position = "bottomright")
At this point, SCtools
extends the analysis from Synth
, While Synth
generates an analysis on one configured dataset, SCtools
provides the tooling to permute the dataset and generate multiple placebos to test the sensitivity of our SCM output.
placebo <- readRDS("basque_placebo.rds")
placebo <- generate.placebos(dataprep.out = dataprep.out, synth.out = synth.out, strategy = "multiprocess")
We can then use the plot_placebos
to run a placebo test for the findings in Abadie and Gardeazabal (2003).
plot_placebos(placebo)
Finally, we can also run the mspe_plot
function to run a post/pre MPSE test for that case, and find how unlikely it would be to find by chance the effects identified.
mspe_plot(placebo)
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