Showing 3 of total 3 results (show query)
s3alfisc
fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models
Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorslinear-regression-modelswild-bootstrapwild-cluster-bootstrapopenblascppopenmp
25 stars 6.69 score 109 scripts 2 dependentss3alfisc
summclust:Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors
Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <arXiv:2205.03288v1>).
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorsfixestlinear-regressionrobust-inference
6 stars 6.16 score 53 scripts 3 dependents
diogoferrari
ceser:Cluster Estimated Standard Errors
Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.
Maintained by Diogo Ferrari. Last updated 3 years ago.
clustered-standard-errorsregression-models
2 stars 3.00 score 5 scripts