Mean Difference in Differences

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Description

MDiD is a Difference in Differences type method for computing the QTET.

The method can accommodate conditioning on covariates though it does so in a restrictive way: It specifies a linear model for outcomes conditional on group-time dummies and covariates. Then, after residualizing (see details in Athey and Imbens (2006)), it computes the Change in Changes model based on these quasi-residuals.

Usage

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MDiD(formla, t, tmin1, tname, x = NULL, data, dropalwaystreated = TRUE,
  panel = FALSE, se = TRUE, plot = FALSE, idname = NULL,
  uniqueid = NULL, alp = 0.05, probs = seq(0.05, 0.95, 0.05),
  iters = 100, seedvec = NULL, printIter = F)

Arguments

formla

The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary)

t

The 3rd time period in the sample (this is the name of the column)

tmin1

The 2nd time period in the sample (this is the name of the column)

tname

The name of the column containing the time periods

x

A vector of covariates (the name of the columns)

data

The name of the data.frame that contains the data

dropalwaystreated

How to handle always treated observations in panel data case (not currently used)

panel

Binary variable indicating whether or not the dataset is panel. This is used for computing bootstrap standard errors correctly.

se

Boolean whether or not to compute standard errors

plot

Boolean whether or not the estimated QTET should be plotted

idname

The individual (cross-sectional unit) id name

uniqueid

Not sure if this is used anymore

alp

The significance level used for constructing bootstrap confidence intervals

probs

A vector of values between 0 and 1 to compute the QTET at

iters

The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE

seedvec

Optional value to set random seed; can possibly be used in conjunction with bootstrapping standard errors.

printIter

Boolean only used for debugging

Value

A QTE object

References

Athey, Susan and Guido Imbens. “Identification and Inference in Nonlinear Difference-in-Differences Models.” Econometrica 74.2, pp. 431-497, 2006.

Thuysbaert, Bram. “Distributional Comparisons in Difference in Differences Models.” Working Paper, 2007.

Examples

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## load the data
data(lalonde)

## Run the Mean Difference in Differences method conditioning on
## age, education, black, hispanic, married, and nodegree
md1 <- MDiD(re ~ treat, t=1978, tmin1=1975, tname="year",
 x=c("age","education","black","hispanic","married","nodegree"),
 data=lalonde.psid.panel, idname="id", se=FALSE,
 probs=seq(0.05, 0.95, 0.05))
summary(md1)