bsfeistest {feisr}

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 (Hausman 1978). Pairs cluster bootstrapping (Cameron et al. 2008; Ruettenauer and Ludwig 2020) 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

Cameron AC, Gelbach JB, Miller DL (2008). “Bootstrap-Based Improvements for Inference with Clustered Errors.” Review of Economics and Statistics, 90(3), 414–427. ISSN 0034-6535, doi: 10.1162/rest.90.3.414.

Hausman JA (1978). “Specification Tests in Econometrics.” Econometrica, 46(6), 1251–1271. ISSN 00129682.

Ruettenauer T, Ludwig V (2020). “Fixed Effects Individual Slopes: Accounting and Testing for Heterogeneous Effects in Panel Data or Other Multilevel Models.” Sociological Methods and Research, OnlineFirst. ISSN 0049-1241, doi: 10.1177/0049124120926211.

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)