boottest.ivreg: Fast wild cluster bootstrap inference for object of class lm

In s3alfisc/fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Models

Fast wild cluster bootstrap inference for object of class lm

Description

boottest.ivreg is a S3 method that allows for fast wild cluster bootstrap inference for objects of class ivreg by implementing the fast wild bootstrap algorithm developed in Roodman et al., 2019 for instrumental variable models (WRE, Davidson & McKinnon, 2010)

Usage

## S3 method for class 'ivreg'
boottest(
  object,
  clustid,
  param,
  B,
  bootcluster = "max",
  conf_int = TRUE,
  R = NULL,
  r = 0,
  sign_level = 0.05,
  type = "rademacher",
  impose_null = TRUE,
  p_val_type = "two-tailed",
  tol = 1e-06,
  floattype = "Float64",
  getauxweights = FALSE,
  maxmatsize = NULL,
  bootstrapc = FALSE,
  liml = FALSE,
  fuller = NULL,
  kappa = NULL,
  arubin = FALSE,
  ssc = boot_ssc(adj = TRUE, fixef.K = "none", cluster.adj = TRUE, cluster.df =
    "conventional"),
  ...
)

Arguments

object	An object of class lm
clustid	A character vector or rhs formula containing the names of the cluster variables
param	A character vector or rhs formula of length one. The name of the regression coefficient for which the hypothesis is to be tested
B	Integer. The number of bootstrap iterations. When the number of clusters is low, increasing B adds little additional runtime
bootcluster	A character vector or rhs formula of length 1. Specifies the bootstrap clustering variable or variables. If more than one variable is specified, then bootstrapping is clustered by the intersections of clustering implied by the listed variables. To mimic the behavior of stata's boottest command, the default is to cluster by the intersection of all the variables specified via the clustid argument, even though that is not necessarily recommended (see the paper by Roodman et al cited below, section 4.2). Other options include "min", where bootstrapping is clustered by the cluster variable with the fewest clusters. Further, the subcluster bootstrap (MacKinnon & Webb, 2018) is supported - see the vignette("fwildclusterboot", package = "fwildclusterboot") for details.
conf_int	A logical vector. If TRUE, boottest computes confidence intervals by test inversion. If FALSE, only the p-value is returned.
R	Hypothesis Vector giving linear combinations of coefficients. Must be either NULL or a vector of the same length as param. If NULL, a vector of ones of length param.
r	A numeric. Shifts the null hypothesis H0: param = r vs H1: param != r
sign_level	A numeric between 0 and 1 which sets the significance level of the inference procedure. E.g. sign_level = 0.05 returns 0.95% confidence intervals. By default, sign_level = 0.05.
type	character or function. The character string specifies the type of boostrap to use: One of "rademacher", "mammen", "norm", "gamma" and "webb". Alternatively, type can be a function(n) for drawing wild bootstrap factors. "rademacher" by default. For the Rademacher and Mammen distribution, if the number of replications B exceeds the number of possible draw ombinations, 2^(#number of clusters), then boottest() will use each possible combination once (enumeration).
impose_null	Logical. Controls if the null hypothesis is imposed on the bootstrap dgp or not. Null imposed (WCR) by default. If FALSE, the null is not imposed (WCU)
p_val_type	Character vector of length 1. Type of p-value. By default "two-tailed". Other options include "equal-tailed", ">" and "<".
tol	Numeric vector of length 1. The desired accuracy (convergence tolerance) used in the root finding procedure to find the confidence interval. Relative tolerance of 1e-6 by default.
floattype	Float64 by default. Other option: Float32. Should floating point numbers in Julia be represented as 32 or 64 bit?
getauxweights	Logical. FALSE by default. Whether to save auxilliary weight matrix (v)
maxmatsize	NULL by default = no limit. Else numeric scalar to set the maximum size of auxilliary weight matrix (v), in gigabytes
bootstrapc	Logical scalar, FALSE by default. TRUE to request bootstrap-c instead of bootstrap-t
liml	Logical scalar. False by default. TRUE for liml or fuller liml
fuller	NULL by default. Numeric scalar. fuller liml factor
kappa	Null by default. fixed <U+03BA> for k-class estimation
arubin	False by default. Logical scalar. TRUE for Anderson-Rubin Test.
ssc	An object of class boot_ssc.type obtained with the function boot_ssc(). Represents how the small sample adjustments are computed. The defaults are ⁠adj = TRUE, fixef.K = "none", cluster.adj = "TRUE", cluster.df = "conventional"⁠. You can find more details in the help file for boot_ssc(). The function is purposefully designed to mimic fixest's fixest::ssc() function.
...	Further arguments passed to or from other methods.

Value

An object of class boottest

p_val	The bootstrap p-value.
conf_int	The bootstrap confidence interval.
param	The tested parameter.
N	Sample size. Might differ from the regression sample size if the cluster variables contain NA values.
boot_iter	Number of Bootstrap Iterations.
clustid	Names of the cluster Variables.
N_G	Dimension of the cluster variables as used in boottest.
sign_level	Significance level used in boottest.
type	Distribution of the bootstrap weights.
impose_null	Whether the null was imposed on the bootstrap dgp or not.
R	The vector "R" in the null hypothesis of interest Rbeta = r.
r	The scalar "r" in the null hypothesis of interest Rbeta = r.
point_estimate	R'beta. A scalar: the constraints vector times the regression coefficients.
grid_vals	All t-statistics calculated while calculating the confidence interval.
p_grid_vals	All p-values calculated while calculating the confidence interval.
t_stat	The 'original' regression test statistics.
t_boot	All bootstrap t-statistics.
regression	The regression object used in boottest.
call	Function call of boottest.
engine	The employed bootstrap algorithm.
nthreads	The number of threads employed.

Setting Seeds

To guarantee reproducibility, you need to set a global random seed via set.seed()

Run boottest quietly

You can suppress all warning and error messages by setting the following global options: options(rlib_warning_verbosity = "quiet") options(rlib_message_verbosity = "quiet") Not that this will turn off all warnings (messages) produced via rlang::warn() and rlang::inform(), which might not be desirable if you use other software build on rlang, as e.g. the tidyverse.

References

Roodman et al., 2019, "Fast and wild: Bootstrap inference in STATA using boottest", The STATA Journal. (https://ideas.repec.org/p/qed/wpaper/1406.html)

Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller. "Bootstrap-based improvements for inference with clustered errors." The Review of Economics and Statistics 90.3 (2008): 414-427.

Cameron, A.Colin & Douglas L. Miller. "A practitioner's guide to cluster-robust inference" Journal of Human Resources (2015) \Sexpr[results=rd]{tools:::Rd_expr_doi("doi:10.3368/jhr.50.2.317")}

Davidson & MacKinnon. "Wild Bootstrap Tests for IV regression" Journal of Economics and Business Statistics (2010) \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1198/jbes.2009.07221")}

MacKinnon, James G., and Matthew D. Webb. "The wild bootstrap for few (treated) clusters." The Econometrics Journal 21.2 (2018): 114-135.

MacKinnon, James G., and Matthew D. Webb. "Cluster-robust inference: A guide to empirical practice" Journal of Econometrics (2022) \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.jeconom.2022.04.001")}

MacKinnon, James. "Wild cluster bootstrap confidence intervals." L'Actualite economique 91.1-2 (2015): 11-33.

Webb, Matthew D. Reworking wild bootstrap based inference for clustered errors. No. 1315. Queen's Economics Department Working Paper, 2013.

Examples

## Not run: 
requireNamespace("ivreg")
requireNamespace("fwildclusterboot")

# 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
)
summary(boot_ivreg)
print(boot_ivreg)
plot(boot_ivreg)
nobs(boot_ivreg)
pval(boot_ivreg)
confint(boot_ivreg)
generics::tidy(boot_ivreg)

## End(Not run)

s3alfisc/fwildclusterboot documentation built on Sept. 17, 2023, 5:55 a.m.