bsfeistest: Bootstrapped Regression Test

In ruettenauer/feisr: Estimating Fixed Effects Individual Slope Models

Bootstrapped Regression Test

Description

Estimates a bootstrapped Hausman test for fixed effects individual slope models.

Usage

bsfeistest(
  model = NA,
  type = c("all", "bs1", "bs2", "bs3"),
  terms = NULL,
  rep = 500,
  seed = NULL,
  prog = TRUE,
  ...
)

Arguments

model	an object of class "feis".
type	one of "all" (the Default), "bs1" for test of FEIS against FE only, "bs2" for test of FE against RE only, and "bs3" for test of FEIS against RE only (see also Details).
terms	An optional character vector specifying which coefficients should be jointly tested. By default, all covariates are included in the Wchi-squared test. For "type=art2", the slope variable is always included in "terms".
rep	the number of repetitions to be used in bootstrapping (default is 500).
seed	the seed used for random sampling in bootstrapping. Needs to be a valid integer. If not specified, the current seed is used.
prog	... logical. If TRUE (the Default) shows the progress in the output window.
...	further arguments.

Details

The function computes a bootstrapped version of the Hausman test \insertCiteHausman.1978.0feisr. Pairs cluster bootstrapping \insertCiteCameron.2008,Ruttenauer.2020feisr is used to obtain the empirical variance-covariance matrix of the estimators, either for FEIS and conventional FE, convention FE and RE, or FEIS and RE.

type="bs1" estimates a bootstrapped Hausman test comparing fixed effects individual slope models and conventional fixed effects models. In this case, bsfeistest tests for inconsistency of the convetional FE model due to heterogeneous slopes. type="bs2" estimates a bootstrapped version of the well-known Hausman test comparing conventional fixed effects models against random effects models. type="bs3" estimates a bootstrapped Hausman directly comparing FEIS against RE, thereby testing for inconsistency of the RE model due to either heterogeneous slopes or time-constant omitted heterogeneity. Bootstrapping is perfomed by resampling with replacement while keeping the number of groups identical to the number of groups in the original dataset. A wald test from aod package is used to perform a Wald chi-squared test on the differences between coefficients.

Value

An object of class "feistest", containing the following elements:

wald_feis	an object of class "wald.test" testing the fixed effects individual slopes model against the conventional fixed effects model (type="bs1").
wald_fe	an object of class "wald.test" testing the fixed effects model against the random effects model (type="bs2").
wald_re	an object of class "wald.test" testing the fixed effects individual slopes model against the random effects model (type="bs3").
vcov1	the empirical (bootstrapped) variance-covariance matrix of the coefficients obtained from FEIS and FE (type="bs1").
vcov2	the empirical (bootstrapped) variance-covariance matrix of the coefficients obtained from FE and RE (type="bs2").
vcov3	the empirical (bootstrapped) variance-covariance matrix of the coefficients obtained from FEIS and RE (type="bs3").
bscoef.feis	a matrix containing the estimated FEIS coefficients of each bootstrap run.
bscoef.fe	a matrix containing the estimated FE coefficients of each bootstrap run.
bscoef.re	a matrix containing the estimated RE coefficients of each bootstrap run.
call	the matched call.
formula	an object of class "Formula" describing the model.
type	the type of performed test(s).
sample	a list containing the IDs sampled in each run.
seed	the seed used for bootstrapping.
terms	character vector of covariates are included in the Wchi-squared test.

References

\insertAllCited

See Also

summary.feistest, feistest, feis, plm, phtest

Examples

data("mwp", package = "feisr")
## Not run: 
feis.mod <- feis(lnw ~ marry + enrol | year,
                 data = mwp, id = "id", robust = TRUE)
bsht <- bsfeistest(feis.mod, type = "bs1", rep = 100, seed = 1234)
summary(bsht)

## End(Not run)

ruettenauer/feisr documentation built on April 5, 2022, 5:43 p.m.