| Title: | Cluster Estimated Standard Errors |
| Version: | 1.0.0 |
| Description: | 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. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| URL: | https://github.com/DiogoFerrari/ceser |
| BugReports: | https://github.com/DiogoFerrari/ceser/issues |
| Depends: | R (≥ 2.10) |
| Imports: | magrittr, purrr, dplyr, tibble, lmtest |
| RoxygenNote: | 7.0.2 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2020-11-04 18:20:22 UTC; diogo |
| Author: | Diogo Ferrari [aut, cre], John Jackson [aut] |
| Maintainer: | Diogo Ferrari <diogoferrari@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2020-11-09 20:20:03 UTC |
Sample data set
Description
A dataset relating the effective number of parties to the number of presidential candidates and presidential power.
Usage
dcese
Format
A data frame with rows and 9 variables:
- country
name of the country
- enep
Effective number of legislative parties
- enpc
Number of presidential candidates
- fapres
Presidential power
- proximity
Proximity of the presidential and legislative elections
- eneg
Eeffective number of ethnic groups
- logmag
log of average district magnitudes
- enpcfapres
Interaction between enpc and fapres
- logmag_eneg
Interaction between logmag and eneg
...
Source
Jackson, John (2019) Corrected Standard Errors with Clustered Data. Political Analysis.
References
Elgie, Robert, Bueur, C., Dolez, B. & Laurent, A. (2014). “Proximity, Candidates, and Presidential Power: How Directly Elected Presidents Shape the Legislative Party System.” Political Research Quarterly. 67(3): 467 - 477.
Cluster Estimated Standard Errors
Description
Cluster Estimated Standard Errors (CESE)
Usage
vcovCESE(mod, cluster = NULL, type = NULL)
Arguments
mod |
a model object. It can be the output of the functions |
cluster |
either a string vector with the name of the variables that will be used to cluster the standard errors, or a formula - e.g., ~ rhs, with a summation of the variables that will be used to cluster the standard errors replacing the |
type |
string with either |
Value
The function returns a variance-covariace matrix of the coefficient estimates using the Cluster Estimated Standard Error (CESE) method.
References
Jackson, John (2019) Corrected Standard Errors with Clustered Data. Political Analysis.
Hayes, A. F., & Cai, L., (2007) Using heteroskedasticity-consistent standard error estimators in ols regression: an introduction and software implementation, Behavior research methods, 39(4), 709–722.
Davidson, R., & MacKinnon, J. G., (2004) Econometric theory and methods: Oxford University Press New York.
Examples
mod = lm(enep ~ enpc + fapres + enpcfapres + proximity + eneg + logmag + logmag_eneg , data=dcese)
## --------------------------------------
## Getting the variance covariance matrix
## --------------------------------------
## Original variance-covariance matrix (no clustered std. errors)
vcov(mod)
## Variance-covariance matrix using CRSE (sandwish package)
## sandwich::vcovCL(mod, cluster = ~ country)
## sandwich::vcovCL(mod, cluster = ~ country, type="HC3")
## Variance-covariance matrix using CESE
ceser::vcovCESE(mod, cluster = ~ country)
ceser::vcovCESE(mod, cluster = ~ country, type="HC3") # HC3 correction
## ---------
## Summaries
## ---------
## no robust SE
summary(mod)
## summary table using CRSE (sandwich package)
## lmtest::coeftest(mod, vcov = sandwich::vcovCL, cluster = ~ country)
## summary using CESE
lmtest::coeftest(mod, vcov = ceser::vcovCESE, cluster = ~ country, type='HC3')