Title: | Weighted Linear Fixed Effects Regression Models for Causal Inference |
---|---|
Description: | Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>. |
Authors: | In Song Kim [aut, cre], Kosuke Imai [aut] |
Maintainer: | In Song Kim <[email protected]> |
License: | GPL(>= 2) |
Version: | 1.9.1 |
Built: | 2024-09-23 05:44:07 UTC |
Source: | https://github.com/insongkim/wfe |
pwfe
is used to fit weighted fixed effects model for causal
inference after transforming outcome variable based on estimated
propensity score. pwfe
also derives the regression weights for
different causal quantity of interest.
pwfe(formula, treat = "treat.name", outcome, data, pscore = NULL, unit.index, time.index = NULL, method = "unit", within.unit = TRUE, qoi = c("ate", "att"), estimator = NULL, C.it = NULL, White = TRUE, White.alpha = 0.05, hetero.se = TRUE, auto.se = TRUE, unbiased.se = FALSE, verbose = TRUE)
pwfe(formula, treat = "treat.name", outcome, data, pscore = NULL, unit.index, time.index = NULL, method = "unit", within.unit = TRUE, qoi = c("ate", "att"), estimator = NULL, C.it = NULL, White = TRUE, White.alpha = 0.05, hetero.se = TRUE, auto.se = TRUE, unbiased.se = FALSE, verbose = TRUE)
formula |
a symbolic description of the model for estimating propensity score. The formula should not include dummmies for fixed effects. The details of model specifications are given under ‘Details’. |
treat |
a character string indicating the name of treatment variable used in the models. The treatment should be binary indicator (integer with 0 for the control group and 1 for the treatment group). |
outcome |
a character string indicating the name of outcome variable. |
data |
data frame containing the variables in the model. |
pscore |
an optional character string indicating the name of estimated propensity score. Note that pre-specified propensity score should be bounded away from zero and one. |
unit.index |
a character string indicating the name of unit variable used in the models. The index of unit should be factor. |
time.index |
a character string indicating the name of time variable used in the models. The index of time should be factor. |
method |
method for weighted fixed effects regression, either
|
within.unit |
a logical value indicating whether propensity score
is estimated within unit. The default is |
qoi |
one of |
estimator |
an optional character string |
C.it |
an optional non-negative numeric vector specifying relative weights for each unit of analysis. |
White |
a logical value indicating whether White misspecification
statistics should be calculated. The default is |
White.alpha |
level of functional specification test. See White
(1980) and Imai . The default is |
hetero.se |
a logical value indicating whether heteroskedasticity
across units is allowed in calculating standard errors. The default
is |
auto.se |
a logical value indicating whether arbitrary
autocorrelation is allowed in calculating standard errors. The
default is |
unbiased.se |
logical. If |
verbose |
logical. If |
To fit the weighted unit (time) fixed effects model with propensity
score weighting, use the syntax for the formula, ~ x1 + x2
,
where x1
and x2
are unit (time) varying
covariates.
One can provide his/her own estimated pscore
which can be used
to transform the outcome varialbe. If so, one does not need to specify
formula
.
If pscore
is not provided, bayesglm
will be used to
estimate propensity scores. If within.unit = TRUE
, propensity
score will be separately estimated within time (unit) when
method
is unit
(time
). Otherwise, propensity
score will be estimated on entire data at once.
The estimated propensity scores will be used to transform the
outcome
variable as described in Imai and Kim (2018).
pwfe
calculates weights based on different underlying causal
quantity of interest: Average Treatment Effect (qoi = "ate"
) or
Average Treatment Effect for the Treated (qoi = "att"
).
One can further set estimating methods: First-Difference
(estimator ="fd"
) or Difference-in-differences (estimator
= "did"
).
To specify different ex-ante weights for each unit of analysis, use
non-negative weights C.it
. For instance, using the survey
weights for C.it
enables the estimation fo the average
treatement effect for the target population.
pwfe
returns an object of class "pwfe", a list that contains the
components listed below.
The function summary
(i.e., summary.pwfe
) can be used to
obtain a table of the results.
coefficients |
a named vector of coefficients |
residuals |
the residuals, that is respons minus fitted values |
df |
the degree of freedom |
W |
weight matrix calculated from the model. Row and column indices can be found from unit.name, time.name. |
call |
the matched call |
causal |
causal quantity of interest |
estimator |
the estimating method |
unit.name |
a vector containing unique unit names |
unit.index |
a vector containing unique unit index number |
time.name |
a vector containing unique time names |
time.index |
a vector containing unique time index number |
method |
call of the method used |
vcov |
the variance covariance matrix |
White.alpha |
the alpha level for White specification test |
White.pvalue |
the p-value for White specification test |
White.stat |
the White statistics |
x |
the design matrix |
y |
the response vector |
mf |
the model frame |
In Song Kim, Massachusetts Institute of Technology, [email protected] and Kosuke Imai, Princeton University, [email protected]
Imai, Kosuke and In Song Kim. (2018) “When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?" American Journal of Political Science, Forthcoming.
Stock, James and Mark Watson. (2008) “Heteroskedasticity-Robust Standard Errors for Fixed Effect Panel Data Regression” Econometrica, 76, 1.
White, Halbert. (1980) 'Using Least Squares to Approximate Unknown Regression Functions.” International Economic Review, 21, 1, 149–170.
wfe
for fitting weighted fixed effect models.
### NOTE: this example illustrates the use of wfe function with randomly ### generated panel data with arbitrary number of units and time. ## generate panel data with number of units = N, number of time = Time ## Not run: N <- 10 # number of distinct units Time <- 15 # number of distinct time ## generate treatment variable treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) ## make sure at least one observation is treated for each unit while ((sum(apply(treat, 2, mean) == 0) > 0) | (sum(apply(treat, 2, mean) == 1) > 0) | (sum(apply(treat, 1, mean) == 0) > 0) | (sum(apply(treat, 1, mean) == 1) > 0)) { treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) } treat.vec <- c(treat) ## unit fixed effects alphai <- rnorm(N, mean = apply(treat, 2, mean)) ## geneate two random covariates x1 <- matrix(rnorm(N*Time, 0.5,1), ncol=N) x2 <- matrix(rbeta(N*Time, 5,1), ncol=N) pscore <- matrix(runif(N*Time, 0,1), ncol=N) x1.vec <- c(x1) x2.vec <- c(x2) pscore <- c(pscore) ## generate outcome variable y <- matrix(NA, ncol = N, nrow = Time) for (i in 1:N) { y[, i] <- alphai[i] + treat[, i] + x1[,i] + x2[,i] + rnorm(Time) } y.vec <- c(y) ## generate unit and time index unit.index <- rep(1:N, each = Time) time.index <- rep(1:Time, N) Data.str <- as.data.frame(cbind(y.vec, treat.vec, unit.index, x1.vec, x2.vec)) colnames(Data.str) <- c("y", "tr", "strata.id", "x1", "x2") Data.obs <- as.data.frame(cbind(y.vec, treat.vec, unit.index, time.index, x1.vec, x2.vec, pscore)) colnames(Data.obs) <- c("y", "tr", "unit", "time", "x1", "x2", "pscore") ############################################################ # Example 1: Stratified Randomized Experiments ############################################################ ## run the weighted fixed effect regression with strata fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. ### Average Treatment Effect ps.ate <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.str, unit.index = "strata.id", method = "unit", within.unit = TRUE, qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.ate) ### Average Treatment Effect for the Treated ps.att <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.str, unit.index = "strata.id", method = "unit", within.unit = TRUE, qoi = "att", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.att) ############################################################ # Example 2: Observational Studies with Unit Fixed-effects ############################################################ ## run the weighted fixed effect regression with unit fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. ### Average Treatment Effect ps.obs <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.obs, unit.index = "unit", time.index = "time", method = "unit", within.unit = TRUE, qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.obs) ## extracting weigths summary(ps.obs)$Weights ### Average Treatment Effect with First-difference ps.fd <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.obs, unit.index = "unit", time.index = "time", method = "unit", within.unit = TRUE, qoi = "ate", estimator = "fd", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.fd) ############################################################ # Example 3: Estimation with pre-specified propensity score ############################################################ ### Average Treatment Effect with Pre-specified Propensity Scores mod.ps <- pwfe(treat = "tr", outcome = "y", data = Data.obs, pscore = "pscore", unit.index = "unit", time.index = "time", method = "unit", within.unit = TRUE, qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(mod.ps) ## End(Not run)
### NOTE: this example illustrates the use of wfe function with randomly ### generated panel data with arbitrary number of units and time. ## generate panel data with number of units = N, number of time = Time ## Not run: N <- 10 # number of distinct units Time <- 15 # number of distinct time ## generate treatment variable treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) ## make sure at least one observation is treated for each unit while ((sum(apply(treat, 2, mean) == 0) > 0) | (sum(apply(treat, 2, mean) == 1) > 0) | (sum(apply(treat, 1, mean) == 0) > 0) | (sum(apply(treat, 1, mean) == 1) > 0)) { treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) } treat.vec <- c(treat) ## unit fixed effects alphai <- rnorm(N, mean = apply(treat, 2, mean)) ## geneate two random covariates x1 <- matrix(rnorm(N*Time, 0.5,1), ncol=N) x2 <- matrix(rbeta(N*Time, 5,1), ncol=N) pscore <- matrix(runif(N*Time, 0,1), ncol=N) x1.vec <- c(x1) x2.vec <- c(x2) pscore <- c(pscore) ## generate outcome variable y <- matrix(NA, ncol = N, nrow = Time) for (i in 1:N) { y[, i] <- alphai[i] + treat[, i] + x1[,i] + x2[,i] + rnorm(Time) } y.vec <- c(y) ## generate unit and time index unit.index <- rep(1:N, each = Time) time.index <- rep(1:Time, N) Data.str <- as.data.frame(cbind(y.vec, treat.vec, unit.index, x1.vec, x2.vec)) colnames(Data.str) <- c("y", "tr", "strata.id", "x1", "x2") Data.obs <- as.data.frame(cbind(y.vec, treat.vec, unit.index, time.index, x1.vec, x2.vec, pscore)) colnames(Data.obs) <- c("y", "tr", "unit", "time", "x1", "x2", "pscore") ############################################################ # Example 1: Stratified Randomized Experiments ############################################################ ## run the weighted fixed effect regression with strata fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. ### Average Treatment Effect ps.ate <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.str, unit.index = "strata.id", method = "unit", within.unit = TRUE, qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.ate) ### Average Treatment Effect for the Treated ps.att <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.str, unit.index = "strata.id", method = "unit", within.unit = TRUE, qoi = "att", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.att) ############################################################ # Example 2: Observational Studies with Unit Fixed-effects ############################################################ ## run the weighted fixed effect regression with unit fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. ### Average Treatment Effect ps.obs <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.obs, unit.index = "unit", time.index = "time", method = "unit", within.unit = TRUE, qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.obs) ## extracting weigths summary(ps.obs)$Weights ### Average Treatment Effect with First-difference ps.fd <- pwfe(~ x1+x2, treat = "tr", outcome = "y", data = Data.obs, unit.index = "unit", time.index = "time", method = "unit", within.unit = TRUE, qoi = "ate", estimator = "fd", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(ps.fd) ############################################################ # Example 3: Estimation with pre-specified propensity score ############################################################ ### Average Treatment Effect with Pre-specified Propensity Scores mod.ps <- pwfe(treat = "tr", outcome = "y", data = Data.obs, pscore = "pscore", unit.index = "unit", time.index = "time", method = "unit", within.unit = TRUE, qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(mod.ps) ## End(Not run)
wfe
is used to fit weighted fixed effects model for causal
inference. wfe
also derives the regression weights for
different causal quantity of interest.
wfe(formula, data, treat = "treat.name", unit.index, time.index = NULL, method = "unit", dyad1.index = NULL, dyad2.index = NULL, qoi = "ate", estimator = NULL, C.it = NULL, hetero.se = TRUE, auto.se = TRUE, dyad.se = FALSE, White = TRUE, White.alpha = 0.05, verbose = TRUE, unbiased.se = FALSE, unweighted = FALSE, store.wdm = FALSE, maxdev.did = NULL, tol = sqrt(.Machine$double.eps))
wfe(formula, data, treat = "treat.name", unit.index, time.index = NULL, method = "unit", dyad1.index = NULL, dyad2.index = NULL, qoi = "ate", estimator = NULL, C.it = NULL, hetero.se = TRUE, auto.se = TRUE, dyad.se = FALSE, White = TRUE, White.alpha = 0.05, verbose = TRUE, unbiased.se = FALSE, unweighted = FALSE, store.wdm = FALSE, maxdev.did = NULL, tol = sqrt(.Machine$double.eps))
formula |
a symbolic description of the model to be fitted. The formula should not include dummmies for fixed effects. The details of model specifications are given under ‘Details’. |
data |
data frame containing the variables in the model. |
treat |
a character string indicating the name of treatment variable used in the models. The treatment should be binary indicator (integer with 0 for the control group and 1 for the treatment group). |
unit.index |
a character string indicating the name of unit variable used in the models. The index of unit should be factor. |
time.index |
a character string indicating the name of time variable used in the models. The index of time should be factor. |
method |
method for weighted fixed effects regression, either
|
dyad1.index |
a character string indicating the variable name of first unit
of a given dyad. The default is |
dyad2.index |
a character string indicating the variable name of second unit
of a given dyad. The default is |
qoi |
one of |
estimator |
an optional character string indicating the
estimating method. One of |
C.it |
an optional non-negative numeric vector specifying relative weights for each unit of analysis. If not specified, the weights will be calculated based on the estimator and quantity of interest. |
hetero.se |
a logical value indicating whether heteroskedasticity
across units is allowed in calculating standard errors. The default
is |
auto.se |
a logical value indicating whether arbitrary
autocorrelation is allowed in calculating standard errors. The
default is |
dyad.se |
a logical value indicating whether correlations across dyads exist. The
default is |
White |
a logical value indicating whether White misspecification
statistics should be calculated. The default is |
White.alpha |
level of functional specification test. See White
(1980) and Imai and Kim (2018). The default is |
verbose |
logical. If |
unbiased.se |
logical. If |
unweighted |
logical. If |
store.wdm |
logical. If |
maxdev.did |
an optional positive numeric value specifying the
maximum deviation in pre-treatment outcome when |
tol |
a relative tolerance to detect zero singular values for generalized inverse. The default is sqrt(.Machine$double.eps) |
To fit the weighted unit (time) fixed effects model, use the syntax
for the formula, y ~ x1 + x2
, where y
is a dependent
variable and x1
and x2
are unit (time) varying
covariates.
wfe
calculates weights based on different underlying causal
quantity of interest: Average Treatment Effect (qoi = "ate"
) or
Average Treatment Effect for the Treated (qoi = "att"
).
One can further set estimating methods: First-Difference
(estimator ="fd"
) or Difference-in-differences (estimator
= "did"
). For the two-way fixed effects model, set estimator
= "did"
To specify different ex-ante weights for each unit of analysis, use
non-negative weights C.it
. For instance, using the survey
weights for C.it
enables the estimation fo the average
treatement effect for the target population.
An object of class "wfe" contains vectors of unique unit(time) names and unique unit(time) indices.
wfe
returns an object of class "wfe", a list that contains the
components listed below.
The function summary
(i.e., summary.wfe
) can be used to
obtain a table of the results.
coefficients |
a named vector of coefficients |
residuals |
the residuals, that is respons minus fitted values |
df |
the degree of freedom |
W |
a dataframe containing unit and time indices along with the
weights used for the observation. If method= |
Num.nonzero |
Number of observations with non-zero weights |
call |
the matched call |
causal |
causal quantity of interest |
estimator |
the estimating method |
units |
a dataframe containing unit names used for |
times |
a dataframe containing time names used for |
method |
call of the method used |
vcov |
the variance covariance matrix |
White.alpha |
the alpha level for White specification test |
White.pvalue |
the p-value for White specification test |
White.stat |
the White statistics |
X |
the design matrix |
Y |
the response vector |
X.wdm |
the demeaned design matrix |
Y.wdm |
the demeaned response vector |
mf |
the model frame where the last column is the weights used for the analysis |
In Song Kim, Massachusetts Institute of Technology, [email protected] and Kosuke Imai, Princeton University, [email protected]
Imai, Kosuke and In Song Kim. (2018) “When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?" American Journal of Political Science, Forthcoming.
Aronow, Peter M., Cyrus Samii, and Valentina A. Assenova (2015) “Cluster–robust Variance Estimation for Dyadic Data." Political Analysis 23, no. 4, 564–577.
Stock, James and Mark Watson. (2008) “Heteroskedasticity-Robust Standard Errors for Fixed Effect Panel Data Regression” Econometrica, 76, 1.
White, Halbert. (1980) “Using Least Squares to Approximate Unknown Regression Functions.” International Economic Review, 21, 1, 149–170.
pwfe
for fitting weighted fixed effects models with propensity
score weighting
### NOTE: this example illustrates the use of wfe function with randomly ### generated panel data with arbitrary number of units and time. ## generate panel data with number of units = N, number of time = Time N <- 10 # number of distinct units Time <- 15 # number of distinct time ## treatment effect beta <- 1 ## generate treatment variable treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) ## make sure at least one observation is treated for each unit while ((sum(apply(treat, 2, mean) == 0) > 0) | (sum(apply(treat, 2, mean) == 1) > 0) | (sum(apply(treat, 1, mean) == 0) > 0) | (sum(apply(treat, 1, mean) == 1) > 0)) { treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) } treat.vec <- c(treat) ## unit fixed effects alphai <- rnorm(N, mean = apply(treat, 2, mean)) ## geneate two random covariates x1 <- matrix(rnorm(N*Time, 0.5,1), ncol=N) x2 <- matrix(rbeta(N*Time, 5,1), ncol=N) x1.vec <- c(x1) x2.vec <- c(x2) ## generate outcome variable y <- matrix(NA, ncol = N, nrow = Time) for (i in 1:N) { y[, i] <- alphai[i] + treat[, i] + x1[,i] + x2[,i] + rnorm(Time) } y.vec <- c(y) ## generate unit and time index unit.index <- rep(1:N, each = Time) time.index <- rep(1:Time, N) Data.str <- as.data.frame(cbind(y.vec, treat.vec, unit.index, x1.vec, x2.vec)) colnames(Data.str) <- c("y", "tr", "strata.id", "x1", "x2") Data.obs <- as.data.frame(cbind(y.vec, treat.vec, unit.index, time.index, x1.vec, x2.vec)) colnames(Data.obs) <- c("y", "tr", "unit", "time", "x1", "x2") ############################################################ # Example 1: Stratified Randomized Experiments ############################################################ ## run the weighted fixed effect regression with strata fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. ### Average Treatment Effect mod.ate <- wfe(y~ tr+x1+x2, data = Data.str, treat = "tr", unit.index = "strata.id", method = "unit", qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(mod.ate) ### Average Treatment Effect for the Treated mod.att <- wfe(y~ tr+x1+x2, data = Data.str, treat = "tr", unit.index = "strata.id", method = "unit", qoi = "att", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(mod.att) ############################################################ # Example 2: Observational Studies with Unit Fixed-effects ############################################################ ## run the weighted fixed effect regression with unit fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. mod.obs <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05) ## summarize the results summary(mod.obs) ## extracting weigths summary(mod.obs)$W ## Not run: ################################################################### # Example 3: Observational Studies with differences-in-differences ################################################################### ## run difference-in-differences estimator. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. mod.did <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", estimator ="did", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05, verbose = TRUE) ## summarize the results summary(mod.did) ## extracting weigths summary(mod.did)$W ######################################################################### # Example 4: DID with Matching on Pre-treatment Outcomes ######################################################################### ## implements matching on pre-treatment outcomes where the maximum ## deviation is specified as 0.5 mod.Mdid <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", estimator ="Mdid", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05, maxdev.did = 0.5, verbose = TRUE) ## summarize the results summary(mod.Mdid) ## Note: setting the maximum deviation to infinity (or any value ## bigger than the maximum pair-wise difference in the outcome) will ## return the same result as Example 3. dev <- 1000+max(Data.obs$y)-min(Data.obs$y) mod.did2 <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", estimator ="Mdid", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05, maxdev.did = dev, verbose = TRUE) ## summarize the results summary(mod.did2) mod.did2$coef[1] == mod.did$coef[1] ## End(Not run)
### NOTE: this example illustrates the use of wfe function with randomly ### generated panel data with arbitrary number of units and time. ## generate panel data with number of units = N, number of time = Time N <- 10 # number of distinct units Time <- 15 # number of distinct time ## treatment effect beta <- 1 ## generate treatment variable treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) ## make sure at least one observation is treated for each unit while ((sum(apply(treat, 2, mean) == 0) > 0) | (sum(apply(treat, 2, mean) == 1) > 0) | (sum(apply(treat, 1, mean) == 0) > 0) | (sum(apply(treat, 1, mean) == 1) > 0)) { treat <- matrix(rbinom(N*Time, size = 1, 0.25), ncol = N) } treat.vec <- c(treat) ## unit fixed effects alphai <- rnorm(N, mean = apply(treat, 2, mean)) ## geneate two random covariates x1 <- matrix(rnorm(N*Time, 0.5,1), ncol=N) x2 <- matrix(rbeta(N*Time, 5,1), ncol=N) x1.vec <- c(x1) x2.vec <- c(x2) ## generate outcome variable y <- matrix(NA, ncol = N, nrow = Time) for (i in 1:N) { y[, i] <- alphai[i] + treat[, i] + x1[,i] + x2[,i] + rnorm(Time) } y.vec <- c(y) ## generate unit and time index unit.index <- rep(1:N, each = Time) time.index <- rep(1:Time, N) Data.str <- as.data.frame(cbind(y.vec, treat.vec, unit.index, x1.vec, x2.vec)) colnames(Data.str) <- c("y", "tr", "strata.id", "x1", "x2") Data.obs <- as.data.frame(cbind(y.vec, treat.vec, unit.index, time.index, x1.vec, x2.vec)) colnames(Data.obs) <- c("y", "tr", "unit", "time", "x1", "x2") ############################################################ # Example 1: Stratified Randomized Experiments ############################################################ ## run the weighted fixed effect regression with strata fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. ### Average Treatment Effect mod.ate <- wfe(y~ tr+x1+x2, data = Data.str, treat = "tr", unit.index = "strata.id", method = "unit", qoi = "ate", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(mod.ate) ### Average Treatment Effect for the Treated mod.att <- wfe(y~ tr+x1+x2, data = Data.str, treat = "tr", unit.index = "strata.id", method = "unit", qoi = "att", hetero.se=TRUE, auto.se=TRUE) ## summarize the results summary(mod.att) ############################################################ # Example 2: Observational Studies with Unit Fixed-effects ############################################################ ## run the weighted fixed effect regression with unit fixed effect. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. mod.obs <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05) ## summarize the results summary(mod.obs) ## extracting weigths summary(mod.obs)$W ## Not run: ################################################################### # Example 3: Observational Studies with differences-in-differences ################################################################### ## run difference-in-differences estimator. ## Note: the quantity of interest is Average Treatment Effect ("ate") ## and the standard errors allow heteroskedasticity and arbitrary ## autocorrelation. mod.did <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", estimator ="did", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05, verbose = TRUE) ## summarize the results summary(mod.did) ## extracting weigths summary(mod.did)$W ######################################################################### # Example 4: DID with Matching on Pre-treatment Outcomes ######################################################################### ## implements matching on pre-treatment outcomes where the maximum ## deviation is specified as 0.5 mod.Mdid <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", estimator ="Mdid", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05, maxdev.did = 0.5, verbose = TRUE) ## summarize the results summary(mod.Mdid) ## Note: setting the maximum deviation to infinity (or any value ## bigger than the maximum pair-wise difference in the outcome) will ## return the same result as Example 3. dev <- 1000+max(Data.obs$y)-min(Data.obs$y) mod.did2 <- wfe(y~ tr+x1+x2, data = Data.obs, treat = "tr", unit.index = "unit", time.index = "time", method = "unit", qoi = "ate", estimator ="Mdid", hetero.se=TRUE, auto.se=TRUE, White = TRUE, White.alpha = 0.05, maxdev.did = dev, verbose = TRUE) ## summarize the results summary(mod.did2) mod.did2$coef[1] == mod.did$coef[1] ## End(Not run)