EquiTrends is an R package for equivalence testing in
the context of Difference-in-Differences estimation. It allows users to
test if pre-treatment trends in the treated group are “equivalent” to
those in the control group. Here, “equivalence” means that rejection of
the null hypothesis implies that a function of the pre-treatment placebo
effects (maximum absolute, average or root mean squared value) does not
exceed a pre-specified threshold below which trend differences are
considered negligible. The package is based on the theory developed in
Dette & Schumann (2024).
The package contains the functions maxEquivTest to
perform the testing procedure surrounding the maximum placebo
coefficient (see equation (3.1) of Dette & Schumann (2024)),
meanEquivTest to perform the testing procedure surrounding
the mean placebo coefficient (see equation (3.2) of Dette & Schumann
(2024)) and
rmsEquivTest to perform the testing procedure surrounding
the root mean squared placebo coefficient (see equation (3.3) and (3.4)
of Dette & Schumann (2024)).
Furthermore, the package contains the function
sim_paneldata to simulate a paneldataset for such testing
purposes.
You can install the development version of EquiTrends
from GitHub
with:
# install.packages("devtools")
devtools::install_github("TiesBos/EquiTrends")The EquiTrends package contains a function to simulate
panel data, tailored to the Difference-in-Differences framework. The
function sim_paneldata simulates a panel dataset with a
given number of individuals \(N\)
(N), number of periods \(T+1\) (in the setting of this package,
indicating the number of pre-treatment periods. In
sim_paneldata \(T+1\) is
referred to as tt), number of covariates \(p\) (p), and treatment
effects. Typically, period \(T+1\) is
referred to as the “base period”. The function also allows for the
simulation of heterogeneity in treatment effects (specified through
eta) and time fixed effects (through lambda).
Furthermore, the function allows for heteroscedasticty (specified
through the binary variable het), serial correlation
(through the AR(1) coefficient phi: \(u_{i,t} = \phi u_{i,t-1} + v_{i,t}\) where
\(v_{i,t}\) follows an i.i.d. \(N(0,\sigma^2)\) distribution and \(\sigma\) is specified through
sd), and clustering in the model errors \(u_{i,t}\). The function returns a data
frame with the following columns: ID (the cross-sectional
individual identifier), period (the time identifier),
Y (the dependent variable), G (a binary vector
indicating if an individual receives treatment, indicated by 1, or not,
indicated by 0), and X_1, X_2, …,
X_p (additional control variables). The construction of the
dependent variable follows the two-way fixed effect model, similar to
the model in equation (2.5) of Dette & Schumann (2024):
\[Y_{i,t} = \eta_i + \lambda_t + \sum_{l=1}^{T}{\beta_l}G_iD_l(t) + X_{1, i, t}\gamma_1+ \dots + X_{p,i,t}\gamma_p +u_{i,t} \quad \text{with} \ \ i=1,...,N, \ \ t=1,...,T+1\]
where \(D_l(t)\) is a dummy variable that equals 1 if \(t=l\) and 0 otherwise. The error-terms \(u_{i,t}\) are generated through a normal distribution with mean 0 and a variance-covariance structure depending on the user-specified parameters. In the following, the \(\beta_l\) coefficients are referred to as placebo coefficients, since they represent the difference in pre-trends between the treatment and control group before treatment has been assigned.
An example of the sim_paneldata function is provided
below:
library(EquiTrends)
# Simulate a panel dataset with 500 individuals, 5 periods, 2 additional
# regressors, and a binary treatment variable without heteroscedasticity,
# serial correlation, and clustering. Furthermore, there are no fixed effects or
# pre-trends in the model (since all values in beta are 0).
sim_data <- sim_paneldata(N = 500, tt = 5, p = 2, beta = rep(0, 5),
gamma = rep(1, 2), het = 0, phi = 0, sd = 1,
burnins = 50)
head(sim_data)
#> ID period Y G X_1 X_2
#> 1 1 1 -0.8123777 0 -0.4407095 -0.655157012
#> 2 1 2 -1.8888861 0 -0.2212108 -0.349846262
#> 3 1 3 -2.2912561 0 -0.9741446 -0.000781637
#> 4 1 4 -0.5314161 0 0.2259398 -1.557426790
#> 5 1 5 -1.5528134 0 -0.1413597 -1.590501621
#> 6 2 1 1.5202663 1 -0.1386675 1.074761245The EquiTrends package contains functions to test for
equivalence of pre-trends in Difference-in-Differences estimation. The
functions rmsEquivTest, meanEquivTest, and
maxEquivTest are used to test for equivalence of pre-trends
in Difference-in-Differences estimation using the placebo coefficients
\(\beta_{l} \ (l=1,...,T)\) estimates.
The functions are based on the work of Dette & Schumann (2024).
rmsEquivTest
functionrmsEquivTest implements the equivalence testing
procedure surrounding the root mean squared placebo coefficient as
described in section 4.2.3 of Dette & Schumann (2024). The
function tests the null hypothesis that the root mean squared placebo
coefficient is larger than or equal to a user-specified equivalence
threshold \(\delta\). That is, if
\[\beta_{RMS} = \sqrt{\frac{1}{T}\sum_{l=1}^{T} \beta_l^2},\]
the tested hypotheses can be represented as
\[H_0: \beta_{RMS} \geq \delta \quad \text{vs.} \quad H_1: \beta_{RMS} < \delta.\]
The null and alternative hypothesis can therefore be seen as
non-negligible and negligible differences in pre-trends, respectively.
The function returns an object of class rmsEquivTest
containing
placebo_coefficients: A numeric vector of the estimated
placebo coefficients,rms_placebo_coefs: The root mean squared value of the
placebo coefficients,significance_level: The significance level of the
test,base_period: The base period used in the testing
procedure,num_individuals: The number of cross-sectional
individuals in the panel used for testing,num_periods: The number of pre-treatment periods in the
panel used for testing (if the panel is unbalanced,
num_periods represents the range in the number of time
periods covered by different individuals),num_observations: The total number of observations in
the panel used for testing,is_panel_balanced: A logical value indicating whether
the used panel is balanced,equiv_threshold_specified: A logical value indicating
whether an equivalence threshold was specified.equiv_threshold_specified = TRUE, then additionally:
rms_critical_value: The critical value at the chosen
significance level,reject_null_hypothesis: A logical value indicating
whether to reject the null hypothesis,equiv_threshold: The equivalence threshold
specified.equiv_threshold_specified = FALSE, then
additionally:
minimum_equiv_threshold: The minimum equivalence
threshold for which the null hypothesis of non-negligible
trend-differences can be rejected.One should note that rows containing NA values are
removed from the panel before the testing procedure is performed.
Please be aware that the equivalence test based on the root mean squared placebo coefficient applies a randomization technique (as described by Dette & Schumann (2024)), leading to a stochastic critical value and minimum equivalence threshold. Therefore, the results may vary between different runs of the function.
# Perform the equivalence test using an equivalence threshold of 1 with periods
# 1-4 as pre-treatment periods based on the RMS testing procedure:
# - option 1: using column names in the panel
# One can use the names of the columns in the panel to specify the variables:
rmsEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c("X_1", "X_2"),
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4)
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Root Mean Squared Placebo Effect
#> Significance level: 0.05
#> Alternative hypothesis: the root mean squared placebo effect does not exceed the equivalence threshold of 1 .
#> ---
#> RMS Placebo Effect Simulated Crit. Val. Reject H0
#> 0.1835 0.9558 TRUE
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000# - option 2: using column numbers in the panel
# Alternatively, one can use the column numbers in the panel to specify the variables:
rmsEquivTest(Y = 3, ID = 1, G = 4, period = 2, X = c(5, 6),
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4)
# - option 3: using separate variables
# One can also use the variables directly without specifying the data variable:
data_Y <- sim_data$Y
data_ID <- sim_data$ID
data_G <- sim_data$G
data_period <- sim_data$period
data_X <- cbind(sim_data$X_1, sim_data$X_2)
rmsEquivTest(Y = data_Y, ID = data_ID, G = data_G, period = data_period, X = data_X,
equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4)The testing procedures can also be performed without specifying the equivalence threshold. Then, the minimum equivalence threshold is returned for which the null hypothesis of non-negligible trend-differences can be rejected. Again, the three possible ways of entering the data as above can be used.
rmsEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c("X_1", "X_2"),
data = sim_data, equiv_threshold = NULL, pretreatment_period = 1:4,
base_period = 4)
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Root Mean Squared Placebo Effect
#> Significance level: 0.05
#> Alternative hypothesis: the root mean squared placebo effect does not exceed the equivalence threshold.
#> ---
#> RMS Placebo Effect Min. Equiv. Threshold
#> 0.1835 0.2558
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000Finally, one should note that the test procedure also works for unbalanced panels.
# To illustrate this, we generate an unbalanced panel dataset by randomly selecting
# 70% of the observations from the balanced panel dataset:
random_indices <- sample(nrow(sim_data), 0.7*nrow(sim_data))
unbalanced_sim_data <- sim_data[random_indices, ]
# With Equivalence Threshold:
rmsEquivTest(Y = 3, ID = 1, G = 4, period = 2, X = c(5, 6),
data = unbalanced_sim_data, equiv_threshold = 1,
pretreatment_period = 1:4, base_period = 4)
# Without Equivalence Threshold:
rmsEquivTest(Y = 3, ID = 1, G = 4, period = 2, X = c(5, 6),
data = unbalanced_sim_data, equiv_threshold = NULL,
pretreatment_period = 1:4, base_period = 4)maxEquivTest
functionThe maxEquivTest function tests the null hypothesis that
the maximum placebo coefficient is larger than or equal to a
user-specified equivalence threshold \(\delta\). That is, if
\[\lVert\beta\rVert_\infty = \max_{l=1,...T} |\beta_l|,\]
the tested hypotheses can be represented as
\[H_0: \lVert\beta\rVert_\infty \geq \delta \quad \text{vs.} \quad H_1: \lVert\beta\rVert_\infty < \delta.\]
The null and alternative hypothesis can therefore be seen as non-negligible and negligible differences in pre-trends, respectively.
The function maxEquivTest contains three testing
procedures for this test, as described in Section 4.2.1. of Dette &
Schumann (2024). The
function allows for the testing of the equivalence of pre-trends using a
bootstrap for spherical errors (type = "Boot"), a wild
bootstrap for clustered standard errors (type = "Wild"),
and an Intersection Union approach (type = "IU") that
rejects the null if all estimates for \(\beta_1,...,\beta_{T}\) are smaller than
their individual critical values. The function returns an object of
class maxEquivTestBoot if type = "Boot" or
type = "Wild" or maxEquivTestIU if
type = "IU". If no type is specified,
maxEquivTest applies the Intersection Union procedure for
efficiency reasons.
maxEquivTest function with
type = "IU"Examples of implementing the Intersection unit test with different
possible variance-covariance matrices (required to perform the test) are
provided below (for more information on the possible variance-covariance
matrices, see the documentation of the maxEquivTest
function). If an equivalence threshold is supplied, the function will
test the previous hypothesis. If no equivalence threshold is supplied,
the function finds the minimum equivalence threshold for which the null
of non-negligible trend-differences can be reject using the Intersection
Union test. The function returns an object of class
maxEquivTestIU containing the following information:
placebo_coefficients: A numeric vector of the estimated
placebo coefficients,abs_placebo_coefficients: A numeric vector with the
absolute values of estimated placebo coefficients,placebo_coefficients_se: A numeric vector with the
standard errors of the placebo coefficients,significance_level: The chosen significance level of
the test,base_period: The base period used in the testing
procedure,placebo_names: The names corresponding to the placebo
coefficients,num_individuals: The number of cross-sectional
individuals in the panel used for testing,num_periods: The number of pre-treatment periods in the
panel used for testing (if the panel is unbalanced,
num_periods represents the range in the number of time
periods covered by different individuals),num_observations: The number of observations in the
panel used for testing,is_panel_balanced: A logical value indicating whether
the panel data is balanced,equiv_threshold_specified: A logical value indicating
whether an equivalence threshold was specified.equiv_threshold_specified = TRUE, then additionally:
IU_critical_values: A numeric vector with the
individual critical values for each of the placebo coefficients,reject_null_hypothesis: A logical value indicating
whether the null hypothesis of negligible pre-trend differences can be
rejected at the specified significance level,equiv_threshold: The equivalence threshold
employed.equiv_threshold_specified = FALSE, then
additionally:
minimum_equiv_thresholds: A numeric vector including
for each placebo coefficient the minimum equivalence threshold for which
the null hypothesis of negligible pre-trend differences can be rejected
for the corresponding placebo coefficient individually,minimum_equiv_threshold: A numeric scalar minimum
equivalence threshold for which the null hypothesis of negligible
pre-trend differences can be rejected for all placebo coefficients.One should note that rows containing NA values are
removed from the panel before the testing procedure is performed.
# Perform the test with equivalent threshold specified as 1 based on
# pre-treatment periods 1-4 and homoscedastic error-terms:
# To select variables, one can use the column names / numbers in the panel data
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = 2, X= c(5,6),
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "IU")
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Intersection Union
#> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold of 1 .
#> Reject null hypothesis: TRUE
#> ( Critical values are printed for the significance level: 0.05 )
#> ---
#> Abs. Estimate Std. Error Critical Value
#> 0.09848 0.1221 0.7992
#> 0.27253 0.1221 0.7992
#> 0.13041 0.1220 0.7993
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000
# Alternatively, one can enter the variables separately:
data_Y <- sim_data$Y
data_ID <- sim_data$ID
data_G <- sim_data$G
data_period <- sim_data$period
data_X <- sim_data[, c(5, 6)]
maxEquivTest(Y = data_Y, ID = data_ID, G = data_G, period = data_period, X = data_X,
equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "IU")
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Intersection Union
#> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold of 1 .
#> Reject null hypothesis: TRUE
#> ( Critical values are printed for the significance level: 0.05 )
#> ---
#> Abs. Estimate Std. Error Critical Value
#> 0.09848 0.1221 0.7992
#> 0.27253 0.1221 0.7992
#> 0.13041 0.1220 0.7993
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000# Perform the test without specifying the equivalence threshold with heteroscedastic
# and autocorrelation robust variance-covariance matrix estimator:
maxEquivTest(Y = 3, ID = 1, G = 4, period = 2,
data = sim_data, equiv_threshold = NULL, pretreatment_period = 1:4,
base_period = 4, type = "IU", vcov = "HAC")
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Intersection Union
#> Significance level: 0.05
#> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold.
#> Minimum equivalence threshold to accept the alternative: 0.4974
#> ---
#> Estimate Std. Error Minimum Equivalence Threshold
#> 0.1028 0.2172 0.4489
#> 0.1558 0.2088 0.4974
#> 0.1257 0.2131 0.4711
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000# Perform the test without specifying the equivalence threshold with a custom
# variance-covariance matrix estimator:
vcov_func <- function(x) {plm::vcovHC(x, method = "white1", type = "HC2")}
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "IU", vcov = vcov_func)
# Perform the test using clustered standard errors based on a vector indicating
# the cluster. For instance, two clusters with the following rule: all
# individuals with an ID below 250 are in the same cluster.
cluster_ind <- ifelse(sim_data$ID < 250, 1, 2)
maxEquivTest(Y = data_Y, ID = data_ID, G = data_G, period = data_period, X = data_X,
equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "IU", vcov = "CL", cluster = cluster_ind)
#> Registered S3 method overwritten by 'clubSandwich':
#> method from
#> bread.mlm sandwichNote that the testing procedure can also handle unbalanced panels.
# To illustrate this, we generate an unbalanced panel dataset by randomly selecting
# 70% of the observations from the balanced panel dataset:
random_indices <- sample(nrow(sim_data), 0.7*nrow(sim_data))
unbalanced_sim_data <- sim_data[random_indices, ]
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c(5, 6),
data = unbalanced_sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "IU", vcov = "HAC")Examples of implementing the bootstrap based test are provided below.
For both type = "Boot" and type = "Wild", an
equivalence threshold is required to perform the test. Furthermore, both
testing procedures return an object of class “maxEquivTestBoot”
containing
placebo_coefficients: A numeric vector of the estimated
placebo coefficients,abs_placebo_coefficients: A numeric vector with the
absolute values of estimated placebo coefficients,max_abs_coefficient: The maximum absolute estimated
placebo coefficient,B: The number of bootstrap samples used to find the
critical value,significance_level: The chosen significance level of
the test,base_period: The base period used in the testing
procedure,placebo_names: The names corresponding to the placebo
coefficients,num_individuals: The number of cross-sectional
individuals in the panel used for testing,num_periods: The number of pre-treatment periods in the
panel used for testing (if the panel is unbalanced,
num_periods represents the range in the number of time
periods covered by different individuals),num_observations: The total number of observations in
the panel used for testing,is_panel_balanced: A logical value indicating whether
the panel data is balanced,equiv_threshold_specified: A logical value indicating
whether an equivalence threshold was specified.equiv_threshold_specified = TRUE, then additionally:
bootstrap_critical_value: The by bootstrap found
critical value for the equivalence test based on the maximum absolute
placebo coefficient,reject_null_hypothesis: A logical value indicating
whether the null hypothesis of negligible pre-trend differences can be
rejected at the specified significance level,equiv_threshold_specified = FALSE, then
additionally:
minimum_equiv_threshold: The minimum equivalence
threshold for which the null hypothesis of negligible pre-trend
differences can be rejected for the bootstrap procedure.One should note that rows containing NA values are
removed from the panel before the testing procedure is performed.
On top of that, please be aware that the bootstrap procedures for the equivalence test based on the maximum absolute placebo coefficient apply a bootstrap procedure (as described by Dette & Schumann (2024)), leading to a stochastic critical value and minimum equivalence threshold. Therefore, the results may vary slightly between different runs of the function.
The bootstrap for spherical errors with 1000 bootstrap iterations:
# Perform the test with equivalence threshold specified as 1 based on
# pre-treatment periods 1:4 (with base period 4) with the general bootstrap procedure:
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "Boot", B = 1000)
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Bootstrap for Spherical Errors (Based on 1000 bootstrap samples)
#> Significance level: 0.05
#> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold of 1 .
#> ---
#> Max. Abs. Coefficient Bootstrap Critical Value Reject H0
#> 0.1558 0.6586 TRUE
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000The Wild boostrap with 1000 bootstrap iterations:
# Perform the test with the equivalence threshold specified as 1 based on
# pre-treatment periods 1:4 (with base period 4) with the wild bootstrap procedure:
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, type = "Wild")
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Cluster Wild Bootstrap (Based on 1000 bootstrap samples)
#> Significance level: 0.05
#> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold of 1 .
#> ---
#> Max. Abs. Coefficient Bootstrap Critical Value Reject H0
#> 0.1558 0.6642 TRUE
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000The bootstrap procedures can handle unspecified equivalence thresholds:
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = sim_data, equiv_threshold = NULL, pretreatment_period = 1:4,
base_period = 4, type = "Boot", B = 1000)
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = sim_data, equiv_threshold = NULL, pretreatment_period = 1:4,
base_period = 4, type = "Wild", B = 1000)The bootstrap procedures can handle unbalanced panels:
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = unbalanced_sim_data, equiv_threshold = 1,
pretreatment_period = 1:4,
base_period = 4, type = "Boot", B = 1000)
maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = unbalanced_sim_data, equiv_threshold = 1,
pretreatment_period = 1:4,
base_period = 4, type = "Wild", B = 1000) meanEquivTest
functionThe meanEquivTest implements the equivalence testing
procedure surrounding the mean placebo coefficient, as described in
Section 4.2.2. of Dette & Schumann (2024). The
function tests the null hypothesis that the absolute mean placebo
coefficient is larger than or equal to a user-specified equivalence
threshold, \(\delta\). That is, if
\[\bar{\beta} = \frac{1}{T}\sum_{l=1}^{T} \beta_l,\]
the tested hypotheses can be represented as
\[H_0: |\bar{\beta}| \geq \delta \quad \text{vs.} \quad H_1: |\bar{\beta}| < \delta.\]
The null and alternative hypothesis can therefore be seen as
non-negligible and negligible differences in pre-trends, respectively.
Implementation of the test is similar to the maxEquivTest
function in terms of the possible variance-covariance matrices (for more
information on the possible variance-covariance matrices, see the
documentation of the meanEquivTest function). The function
returns an object of class meanEquivTest containing
placebo_coefficients: A numeric vector of the estimated
placebo coefficients,abs_mean_placebo_coefs: The absolute value of the mean
of the placebo coefficients,var_mean_placebo_coef: The estimated variance of the
mean placebo coefficient,significance_level: The significance level of the
test,base_period: The base period used in the testing
procedure,num_individuals: The number of cross-sectional
individuals in the panel used for testing,num_periods: The number of pre-treatment periods in the
panel used for testing (if the panel is unbalanced,
num_periods represents the range in the number of time
periods covered by different individuals)num_observations: The total number of observations in
the panel used for testing,is_panel_balanced: A logical value indicating whether
the panel is balanced,equiv_threshold_specified: A logical value indicating
whether an equivalence threshold was specified.equiv_threshold_specified = TRUE, then additionally:
mean_critical_value: The critical value at the chosen
significance level,p_value: The p-value of the test,reject_null_hypothesis: A logical value indicating
whether to reject the null hypothesis,equiv_threshold: The equivalence threshold
specified.equiv_threshold_specified = FALSE, then
additionally:
minimum_equiv_threshold: The minimum equivalence
threshold for which the null hypothesis of non-negligible (based on the
equivalence threshold) trend-differences can be rejected.One should note that rows containing NA values are
removed from the panel before the testing procedure is performed.
# Perform the test with equivalent threshold specified as 1 based on
# pre-treatment periods 1-4 and assuming homoscedastic error-terms:
# To select variables, one can use the column names / column numbers in the panel data:
meanEquivTest(Y = "Y", ID = "ID", G = "G", period = 2, X = c(5, 6),
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4)
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Mean Placebo Effect
#> Alternative hypothesis: the mean placebo effect does not exceed the equivalence threshold of 1 .
#> ---
#> Abs. Mean Placebo Effect Std. Error p-value Reject H0
#> 0.1671 0.09965 <2e-16 TRUE
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000 # Alternatively, one can use separate variables:
data_Y <- sim_data$Y
data_ID <- sim_data$ID
data_G <- sim_data$G
data_period <- sim_data$period
data_X <- sim_data[, c(5, 6)]
meanEquivTest(Y = data_Y, ID = data_ID, G = data_G, period = data_period, X = data_X,
equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4)# Perform the test with a heteroscedastic and autocorrelation robust
# variance-covariance matrix estimator, and without specifying the equivalence threshold:
meanEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c(5, 6),
data = sim_data, equiv_threshold = NULL, pretreatment_period = 1:4,
base_period = 4, vcov = "HAC")
#>
#> ==================================================
#> Equivalence Tests for Pre-trends in DiD Estimation
#> ==================================================
#> Type: Mean Placebo Effect
#> Significance level: 0.05
#> Alternative hypothesis: the mean placebo effect does not exceed the equivalence threshold.
#> ---
#> Abs. Mean Placebo Effect Std. Error Min. Equiv. Threshold
#> 0.1671 0.09691 0.3265
#> ---
#> No. placebo coefficients estimated: 3
#> Base period: 4
#>
#> Balanced Panel:
#> + No. pre-treatment periods: 4
#> + No. individuals: 500
#> + Total no. observations: 2000# Perform the test with an equivalence threshold of 1 and a custom
# variance-covariance matrix estimator:
vcov_func <- function(x) {plm::vcovHC(x, method = "white1", type = "HC2")}
meanEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
data = sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, vcov = vcov_func)
# Perform the test using clustered standard errors based on a vector indicating
# the cluster. For instance, two clusters with the following rule: all
# individuals with an ID below 250 are in the same cluster:
cluster_ind <- ifelse(sim_data$ID < 250, 1, 2)
meanEquivTest(Y = data_Y, ID = data_ID, G = data_G, period = data_period, X = data_X,
equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, vcov = "CL", cluster = cluster_ind)Note that the testing procedure can also handle unbalanced panels:
# Finally, one should note that the test procedure also works for unbalanced panels.
# To illustrate this, we generate an unbalanced panel dataset by randomly selecting
# 70% of the observations from the balanced panel dataset:
random_indices <- sample(nrow(sim_data), 0.7*nrow(sim_data))
unbalanced_sim_data <- sim_data[random_indices, ]
meanEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c(5, 6),
data = unbalanced_sim_data, equiv_threshold = 1, pretreatment_period = 1:4,
base_period = 4, vcov = "HAC")Dette H., & Schumann M. (2024). “Testing for Equivalence of Pre-Trends in Difference-in-Differences Estimation.” Journal of Business & Economic Statistics, 1–13. DOI: 10.1080/07350015.2024.2308121