## fwildclusterboot 0.13

### Potentially Breaking Changes:

• boottest(), mboottest() and boot_aggregate()no longer have a dedicated seed argument. From version 0.13, reproducibility of results can only be controlled by setting a global seed via drqng::dqset.seed() and set.seed(). For more context, see the discussion below. As a consequence, results produced via old versions of fwildlcusterboot are no longer exactly reproducible.

• When the bootstrap is run via engine = "WildBootTests.jl", the bootstrapped t-statistics and the original t-statistic are now returned as vectors (to align with the results from other enginges). Previously, they were returned as matrices.

### Other Changes:

• boottest() receives a new argument, sampling, which controls if random numbers are drawn via functions from base or the dqrng package.
• Some code refactoring. All bootstrap algorithms and their associated files have been renamed (e.g. boot_algo2.R is not called boot_algo_fastnwild.R, etc.).
• Much nicer error and message formatting, via rlang::abort(), warn() and inform(). rlang is added as a dependency.

### Background on the Change to Seeding

Prior to the changes introduced in v0.13, boottest() will always call set.seed() or dqrng::dqset.seed() internally, regardless of whether the seed argument is specified or not (in the ladder case, it will create an internal seed by randomly drawing from a large set of integers). I consider this harmless, as setting seeds inside boottest() in this way does not affect the reproducibility of scripts run end-to-end.

However, I have learned that is generally considered bad practice to overwrite global variables without notification - for example, the authors of numpy have deprecated their np.random.seed() function for this reason.

Here is a quick example on what happens if a function “reseeds”: it affects the future chain of random draws.


fn_reseed <- function(x){set.seed(x)}

set.seed(123)
rnorm(1)
# [1] -0.5604756
fn_reseed(1)
rnorm(1)
# [1] -0.6264538

set.seed(123)
rnorm(1); rnorm(1)
# [1] -0.5604756
# [1] -0.2301775

The two ‘second’ calls to rnorm(1) are based on different global seed states.

As a result, I have decided to deprecate the seed' function argument. Random number generation must now **be** set outside ofboottest()usingset.seed()anddqrng::dqset.seed().

This means that bootstrap results generated via versions < 0.13 will no longer be exactly replicable under the new version, but with a sufficiently large number of bootstrap iterations, this change should not affect any of your conclusions.

## fwildclusterboot 0.12.1

CRAN release: 2023-01-23

This is a hot-fix release which turns of tests on CRAN that fail in non-standard CRAN test environments.

## fwildclusterboot 0.12

CRAN release: 2022-10-15

This is the first CRAN release since version 0.9. It comes with a set of new features, but also potentially breaking changes. This section summarizes all developments since version 0.9.

#### Potentially breaking changes:

• boottest()'s function argument boot_algo has been renamed to engine
• the setBoottest_boot_algo() function was renamed to setBoottest_engine()

#### Bug fixes and internal changes

• When a multi-parameter hypothesis of the form R beta = r was tested, the heteroskedastic wild bootstrap would nevertheless always test “beta_k = 0” vs “beta_k != 0”, with “beta_k = param”. I am sorry for that bug!
• The Matrix.utils package is at danger of CRAN removal - it has been replaced by custom functions for internal use.

#### New features and Improvements

• A new function argument has been added - bootstrap_type. In combination with the impose_null function argument, it allows to choose between different cluster bootstrap types - WCx11, WCx13, WCx31, WCx33. For more details on these methods, see the working paper by MacKinnon, Nielsen & Webb (2022). Currently, these new bootstrap types only compute p-values. Adding support for confidence intervals is work in progress.
• A boot_aggregate() method now supports the aggregation of coefficients in staggered difference-in-differences following the methods by Sun & Abraham (2021, Journal of Econometrics) in combination with the sunab() function from fixesthas been added. Essentially, boot_aggregate() is a copy of aggregate.fixest: the only difference is that inference is powered by a wild bootstrap.
• The heteroskedastic bootstrap is now significantly faster, and WCR21 and WCR31 versions are now supported (i.e. HC2 and HC3 ‘imposed’ on the bootstrap dgp.)

## fwildclusterboot 0.11.3

• significant speed improvements for the heteroskedastic bootstrap

## fwildclusterboot 0.11.2

• significant speed improvements for the x1 bootstrap algorithms, bootstrap_type %in% c("11", "31"), both for WCR and WCU

## fwildclusterboot 0.11.1

#### New bootstrap algorithms following MNW (2022)

• A new function argument has been added - bootstrap_type. In combination with the impose_null function argument, it allows to choose between different cluster bootstrap types - WCx11, WCx13, WCx31, WCx33. For more details on these methods, see the working paper by MacKinnon, Nielsen & Webb (2022).

#### boot_aggregate() method for Sun-Abrahams Event Studies

A boot_aggregate() method to supports the aggregation of coefficients in staggered difference-in-differences following the methods by Sun & Abraham (2021, Journal of Econometrics) in combination with the sunab() function from fixesthas been added. Essentially, boot_aggregate() is a copy of aggregate.fixest: the only difference is that inference is powered by a wild bootstrap.

#### Other syntax changes, potentially breaking!

• The boot_algo function argument has been renamed to engine.
• The setBoottest_boot_algo() function has been renamed to setBoottest_engine(). In consequence, the syntax introduced in 0.11 changes to
boottest(
lm_fit,
param = ~treatment,
clustid = ~group_id1,
B = 9999,
impose_null = TRUE,
engine = "R",
bootstrap_type = "11"
)

To run everything through WildBootTests.jl, you would have to specify

boottest(
lm_fit,
param = ~treatment,
clustid = ~group_id1,
B = 9999,
impose_null = TRUE,
engine = "WildBootTests.jl",
bootstrap_type = "11"
)

## fwildclusterboot 0.11

• This release introduces new wild cluster bootstrap variants as described in MacKinnon, Nielsen & Webb (2022). The implementation is still quite bare-bone: it only allows to test hypotheses of the form βk = 0 vs βk ≠ 0, does not allow for regression weights or fixed effects, and further does not compute confidence intervals.

You can run one of the ‘new’ variants - e.g. the “WCR13”, by specifying the bootstrap_type function argument accordingly:

boottest(
lm_fit,
param = ~treatment,
clustid = ~group_id1,
B = 9999,
impose_null = TRUE,
engine = "R",
bootstrap_type = "31"
)

## fwildclusterboot 0.10

• introduces a range of new methods: nobs(), pval(), teststat(), confint() and print()
• multiple (internal) changes for ropensci standards alignment
• drop the t_boot (teststat_boot) function arguments -> they are now TRUE by default
• fix a bug in the lean algorithms - it always tested hypotheses of the form beta = 0 instead of R’beta = r, even when R != 1 and r != 0
• enable full enumeration for R-lean tests
• enable deterministic ‘full enumeration tests’ - these are exact

## fwildclusterboot 0.9

CRAN release: 2022-06-10

• 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 0.8 CRAN release: 2022-04-18 #### Two new bootstrap algorithms: ‘WildBootTests.jl’ and ‘R-lean’ ##### boot_algo = ‘WildBootTests.jl’ • 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 <- na.omit(SchoolingReturns) 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 0.2128746 • For 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. ##### boot_algo = ‘R-lean’ 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" ) #### Heteroskeadstic Wild Bootstrap 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 deprecated For consistency with WildBootTests.jl, the boottest() function argument beta0 is now replaced by a new function argument, r. #### Frühjahrsputz I have spent some time to clean up fwildclusterboot's internals, which should now hopefully be more readable and easier to maintain. #### Testing 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. ## fwildclusterboot 0.7 CRAN release: 2022-01-03 • Bug fixes, see issues #26 and #27 regarding preprocessing for fixest when weights are passed to feols() as a formula or when cluster is specified in fixest as a column vector. ## fwildclusterboot 0.6 • 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.

## fwildclusterboot 0.5.1

CRAN release: 2021-11-06

• Fixes a bug with Mammen weights introduced in version 0.5 -> switch back to 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().
• Deletes some unnecessary computations from boot_algo2() -> speed improvements
• For B = 2^(#number of clusters), Rademacher weights should have been enumerated - instead, they were drawn randomly and enumeration only occured for B > 2^(#number of clusters). Now, enumeration occurs if B >= 2^(#number of clusters).

## fwildclusterboot 0.5

CRAN release: 2021-11-03

• Version 0.5 fixes an error for the bootstrap with weighted least squares introduced with version 0.4. All unit tests that compare fwildclusterboot with weighted least squares results from boottest.stata pass. In particular, enumerated cases pass with exact equality (in such cases, the bootstrap weights matrices are exactly identical in both R and Stata).
• 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.
• To align 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.
• Sampling of the bootstrap weights is now powered by the 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.

## fwildclusterboot 0.3.7

CRAN release: 2021-09-14

• Bug fix: the output of 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.
• Some tests have been added that compare output from boottest() with the wild cluster bootstrap implemented via clusterSEs.

## fwildclusterboot 0.3.6

CRAN release: 2021-08-01

• Bug fix regarding suggested packages and CRAN: see github issue #12. Added 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.

## fwildclusterboot 0.3.5

CRAN release: 2021-06-20

• 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 = (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.

## fwildclusterboot 0.3.4

CRAN release: 2021-05-01

• Fix CRAN errors caused by a small bug in the vignette

## fwildclusterboot 0.3.3

CRAN release: 2021-04-12

• implements full enumeration for Rademacher and Mammen Weights if 2k < B, where k is the number of clusters and B the number of bootstrap iterations

## fwildclusterboot 0.3.2

CRAN release: 2021-02-26

• Fixes a CRAN test error message for Oracle Solaris.

## fwildclusterboot 0.3.1

CRAN release: 2021-02-16

• A glance.boottest() method was added, which enables the use of the modelsummary package with fwildclusterboot.
• The 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).

## fwildclusterboot 0.3.0

• Additional performance improvements through parallelization. By default, boottest() uses half the available threads for parallel execution. The number of threads can be set via the nthreads function argument.
• Additional function arguments for 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.
• The package no longer depends on data.table and fabricatr` - both are now only suggested. Further, the package now comes with an example data set ‘voters’.