# fwildclusterboot 0.7

• Bug fixes, see issues (#26)[https://github.com/s3alfisc/fwildclusterboot/issues/26] and (#27)[https://github.com/s3alfisc/fwildclusterboot/issues/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

• 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

• 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.

# fwilclusterboot 0.4

• New feature I: 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.
• New feature II: boottest() now also supports “equal-tailed” p-values and one-sided hypotheses. For one-sided tests, confidence intervals are currently not supported.
• Internal changes: To allow for multivariable tests, the boot_algo2() function has slightly been modified. invert_p_val2() is superseded by invert_p_val().
• Further, a CRAN error is fixed - some tests for exact equality failed with relative difference e-05 on openBLAS. In consequence, all exact tests are set to reltol = 1e-04.

# fwildclusterboot 0.3.7

• 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

• 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

• 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.

# fwildclusterboot 0.3.4

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

# fwildclusterboot 0.3.3

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

# fwildclusterboot 0.3.2

• Fixes a CRAN test error message for Oracle Solaris.

# fwildclusterboot 0.3.1

• 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’.