v0.9 moves data pre-processing from model.frame
methods to model_matrix methods. I had wanted to do so for
a while, but issue #42, as raised by Michael Topper, has finally
convinced me to start working on this project.
Moving to model_matrix methods unlocks new
functionality for how boottest() plays with
fixest objects - it is now possible to run
boottest() after feols() models that use
syntactic sugar:
library(fwildclusterboot)
library(fixest)
data(voters)
feols_fit <- feols(proposition_vote ~ i(treatment, ideology1) ,
data = voters
)
boot1 <- boottest(feols_fit,
B = 9999,
param = "treatment::0:ideology1",
clustid = "group_id1"
)
feols_fits <- fixest::feols(proposition_vote ~ treatment | sw(Q1_immigration, Q2_defense), data = voters)
res <- lapply(feols_fits, \(x) boottest(x, B = 999, param = "treatment", clustid = "group_id1"))
voters$split <- sample(1:2, nrow(voters), TRUE)
feols_fits <- fixest::feols(proposition_vote ~ treatment, split = ~split, data = voters)
res <- lapply(feols_fits, \(x) boottest(x, B = 999, param = "treatment", clustid = "group_id1")) Some formula sugar still leads to errors, e.g.
feols_fit2 <- feols(proposition_vote ~ treatment | Q1_immigration^Q2_defense,
data = voters
)
boot1 <- boottest(feols_fit2,
B = 9999,
param = "treatment",
clustid = "group_id1"
)The release further fixes a multicollinearity bug that occured
when lm() or fixest() silently deleted
multicollinar variable(s). Thanks to Kurt Schmidheiny for
reporting!
The na_omit function argument has been dropped. If
the cluster variable is not included in the regression model, it is now
not allowed to contain NA values.
Several function arguments can now be fed to
boottest() as formulas (param,
clustid, bootcluster,
fe).
data(voters)
feols_fit <- feols(proposition_vote ~ treatment ,
data = voters
)
boot <- boottest(feols_fit,
B = 9999,
param = ~ treatment,
clustid = ~ group_id1
)fwildclusterboot now supports calling WildBootTests.jl,
which is a very fast Julia implementation of the wild cluster bootstrap
algorithm. To do so, a new function argument is introduced,
boot_algo, through which it is possible to control the
executed bootstrap algorithm.# load data set voters included in fwildclusterboot
data(voters)
# estimate the regression model via lm
lm_fit <- lm(proposition_vote ~ treatment + ideology1 + log_income + Q1_immigration , data = voters)
boot_lm <- boottest(
lm_fit,
clustid = "group_id1",
param = "treatment",
B = 9999,
boot_algo = "WildBootTests.jl"
)WildBootTests.jl is (after compilation) orders of magnitudes
faster than fwildclusterboot's native R implementation, and
speed gains are particularly pronounced for large problems with a large
number of clusters and many bootstrap iterations.
Furthermore, WildBootTests.jl supports a range of
models and tests that were previously not supported by
fwildclusterboot: most importantly a) wild cluster
bootstrap tests of multiple joint hypotheses and b) the WRE bootstrap by
Davidson & MacKinnon for instrumental variables estimation. On top
of the cake … the WRE is really fast.
library(ivreg)
data("SchoolingReturns", package = "ivreg")
# drop all NA values from SchoolingReturns
SchoolingReturns <- SchoolingReturns[rowMeans(sapply(SchoolingReturns, is.na)) == 0,]
ivreg_fit <- ivreg(log(wage) ~ education + age + ethnicity + smsa + south + parents14 |
nearcollege + age + ethnicity + smsa + south + parents14, data = SchoolingReturns)
boot_ivreg <- boottest(
object = ivreg_fit,
B = 999,
param = "education",
clustid = "kww",
type = "mammen",
impose_null = TRUE
)
generics::tidy(boot_ivreg)
# term estimate statistic p.value conf.low conf.high
# 1 1*education = 0 0.0638822 1.043969 0.2482482 -0.03152655 0.2128746For guidance on how to install and run
WildBooTests.jl, have a look at the associated article.
Also, note that running the wild cluster bootstrap through
WildBootTests.jl is often very memory-efficient.
A key limitation of the vectorized ‘fast’ cluster bootstrap algorithm
as implemented in fwildclusterboot is that it is very
memory-demanding. For ‘larger’ problems, running boottest()
might lead to out-of-memory errors. To offer an alternative,
boottest() now ships a ‘new’ rcpp- and loop-based
implementation of the wild cluster bootstrap (the ‘wild2’ algorithm in
Roodman et al).
boot_lm <- boottest(
lm_fit,
clustid = "group_id1",
param = "treatment",
B = 9999,
boot_algo = "R-lean"
)It is now possible to run boottest() without specifying
a clustid function argument. In this case,
boottest() runs a heteroskedasticity-robust wild bootstrap
(HC1), which is implemented in c++.
boot_hc1 <- boottest(lm_fit, param = "treatment", B = 9999)
summary(boot_hc1)boottest()
function argument beta0 deprecatedFor consistency with WildBootTests.jl, the
boottest() function argument beta0 is now
replaced by a new function argument, r.
I have spent some time to clean up fwildclusterboot's
internals, which should now hopefully be more readable and easier to
maintain.
fwildclusterboot is now pre-dominantly tested against
WildBootTests.jl. Tests that depend on Julia are by default
not run on CRAN, but are regularly run on Mac, Windows and Linux via github
actions.
Bug fix: for one-sided hypotheses for the WRU bootstrap (if impose_null = FALSE), the returned p-values were incorrect - they were reported as ‘p’, but should have been ‘1-p’. E.g. if the reported p-values was reported as 0.4, it should have been reported as 0.6.
A new function argument ssc gives more control over
the small sample adjustments made within boottest(). It
closely mirrors the ssc argument in fixest.
The only difference is that fwildclusterboot::boot_ssc()'s
fixef.K argument currently has only one option,
'none', which means that the fixed effect parameters are
discarded when calculating the number of estimated parameters k. The
default argument of boot_ssc() are
adj = TRUE, fixef.K = "none", cluster.adj = TRUE and
cluster.df = "conventional". In fixest, the
cluster.df argument is "min" by default. Prior
to v 0.6, by default, no small sample adjustments regarding the sample
size N and the number of estimated parameters k were applied. The
changes in v0.6 may slightly affect the output of
boottest(). For exact reproducibility of previous results,
set adj = FALSE. Setting adj = TRUE will not
affect p-values and confidence intervals for oneway clustering,
but the internally calculated t-stat, which is divided by \(\sqrt{(N-k)/(N-1)}\). For twoway
clustering, it might affect the number and order of invalid bootstrapped
t-statistics (due to non-positive definite covariance matrices) and,
through this channel, affect bootstrapped inferential
parameters.
Testing: unit tests are now run on github actions against wildboottestjlr, which is a JuliaConnectoR based wrapper around WildBootTests.jl, a Julia implementation of the fast wild cluster bootstrap algorithm.
Additionally, minor speed tweaks.
sample() function. To guarantee
reproducibilty with Mammen weights, either a seed needs to be specified
in boottest() or a global seed needs to be set via
set.seed().boottest() now stops if fixest::feols()
deletes non-NA values (e.g. singleton fixed effects deletion) and asks
the user to delete such rows prior to estimation via
feols() & boottest(). Currently,
boottest()'s pre-processing cannot handle such deletions -
this remains future work.fwildclusterboot with Stata’s boottest command
(Roodman et al, 2019), Mammen weights are no longer enumerated in
fwildclusterboot::boottest().boottest() no longer sets an internal seed (previously
set.seed(1)) if no seed is provided as a function argument.dqrng package, which speeds up the creation of the
bootstrap weights matrix. To set a “global” seed, one now has use the
dqset.seed() function from the dqrng package,
which is added as a dependency.boottest() now allows for univariate
tests that involve multiple variables. E.g. one can now test hypothesis
as var1 + var2 = c where c is a scalar. More details on the
syntax can be found in the vignette. All methods of for objects of class
boottest have been updated.boottest() now also supports
“equal-tailed” p-values and one-sided hypotheses. For one-sided tests,
confidence intervals are currently not supported.boot_algo2() function has slightly been modified.
invert_p_val2() is superseded by
invert_p_val().dreamerr
package.boottest() varied depending on
the class of the input fixed effects for regressions both via
lfe::felm() and fixest::feols(). This bug
occurred because boottest() does not work with a
pre-processed model.frame object from either felm() or
feols() but works with the original input data. While both
felm() and feols() change non-factor fixed
effects variables to factors internally, boottest() did not
check but implicitely assumed that all fixed effects used in the
regression models are indeed factors in the original data set. As a
consequence, if one or more fixed effects were e.g. numeric,
boottest() would produce incorrect results without throwing
an error. With version 0.3.7, boottest() checks internally
if all variables in the original data set which are used as fixed
effects are factor variables and if not, changes them to factors. Thanks
for timotheedotc for raising the issue on github, which can be found
here: https://github.com/s3alfisc/fwildclusterboot/issues/14.boottest() with the wild cluster bootstrap implemented via
clusterSEs.if(requireNamespace("pkgname"))
statements for suggested packages in the vignettes, examples and tests.
Note that unit tests will now only execute on CRAN if both
fixest and lfe can be installed on the
OS.Bug fix: For Rademacher and Mammen weights and cases where (2^
number of clusters) < # boostrap iterations, (deterministic ) full
enumeration should have been employed for sampling the bootstrap
weights. Full enumeration means the following: for e.g. 6 numbers of
clusters, only 2^6 = 64 unique draws from either the Rademacher or
Mammen distributions exists. Therefore, boottest()
overwrites the user-provided number of bootstrap iterations to \(B = \text{(2^ number of clusters)}\) if a
larger number is chosen. The bug now occured because the bootstrap
weights were drawn randomly with replacement instead of
using full enumeration. Note: full enumeration was
introduced with version 0.3.3. Thanks to fschoner for finding the bug!
see
github issue #11
Bug fix: A small bug has been fixed related to missing values in the cluster variables.
By default, boottest() now sets an internal seed if
no seed is provided by the user via the seed function
argument.
Several improvements to the documentation.
glance.boottest() method was added, which enables the
use of the modelsummary package with
fwildclusterboot.tidy.boottest() method is no longer exported. You
can still access it via fwildclusterboot:::tidy.boottest()
or by loading the generics package via
library(generics).boottest() uses half the available threads for
parallel execution. The number of threads can be set via the
nthreads function argument.boottest() - the user
can now set the tolerance and maximum number of iterations for the
calculation of confidence intervals. By default, tol = 1e-6
and maxiter = 10.data.table and
fabricatr - both are now only suggested. Further, the
package now comes with an example data set ‘voters’.Add support for