hcinfer computes heteroskedasticity-consistent covariance estimators
and normal Wald inference for ordinary least squares models. The
currently implemented covariance matrix estimators are listed below.
The table below is generated by hc_methods() and lists the covariance
matrix estimators currently implemented in hcinfer.
| type | label | description | default_arguments |
|---|---|---|---|
| hc0 | HC0 | White heteroskedasticity-consistent estimator. | none |
| hc1 | HC1 | HC0 with degrees-of-freedom scaling. | none |
| hc2 | HC2 | Leverage-adjusted estimator with exponent 1. | none |
| hc3 | HC3 | Leverage-adjusted estimator with exponent 2. | none |
| hc4 | HC4 | Adaptive leverage correction by Cribari-Neto. | none |
| hc4m | HC4m | Modified HC4 correction by Cribari-Neto and da Silva. | none |
| hc5 | HC5 | High-leverage correction by Cribari-Neto, Souza, and Vasconcellos. | k = 0.7 |
| hc5m | HC5m | Modified HC5 correction by Li, Zhang, Zhang, and Wang. | k = 0.7, k1 = 1, k2 = 0, k3 = 1, gamma1 = 1, gamma2 = 1.5 |
| hcbeta | HCbeta | Beta-distribution leverage correction. | c1 = 7, c2 = 0.75, lower = 0.01, upper = 0.99 |
# Official CRAN installation of the package
install.packages("hcinfer")
# r-universe installation
install.packages('hcinfer', repos = c('https://prdm0.r-universe.dev', 'https://cloud.r-project.org'))
# Development version installation from GitHub
remotes::install_github("prdm0/hcinfer", force = TRUE)library(hcinfer)
schools <- PublicSchools
schools$income_scaled <- schools$income / 10000
schools$income_scaled_sq <- schools$income_scaled^2
fit <- lm(expenditure ~ income_scaled + income_scaled_sq, data = schools)
result <- hcinfer(fit)The default estimator is HCbeta. Use tests() and confint() to
extract the main inferential quantities as tibbles.
tests(result)
#> # A tibble: 3 × 8
#> term estimate null_value std_error z_value p_value alpha reject
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 (Intercept) 833. 0 851. 0.979 0.328 0.05 FALSE
#> 2 income_scaled -1834. 0 2309. -0.794 0.427 0.05 FALSE
#> 3 income_scaled_sq 1587. 0 1547. 1.03 0.305 0.05 FALSE
confint(result)
#> # A tibble: 3 × 4
#> term conf_low conf_high level
#> <chr> <dbl> <dbl> <dbl>
#> 1 (Intercept) -834. 2500. 0.95
#> 2 income_scaled -6359. 2691. 0.95
#> 3 income_scaled_sq -1446. 4620. 0.95The plot() method displays the robust confidence intervals and marks
the null value used in the tests.
plot(result)
Use vcov_hc() when you only need the robust covariance matrix and its
diagnostics. The plot() method for this object shows leverage values
and HC adjustment factors.
cov_hcbeta <- vcov_hc(fit)
plot(cov_hcbeta)
hc_methods()
coef(result)
vcov(result)The most common workflow is:
fit <- lm(y ~ x1 + x2, data = data)
result <- hcinfer(fit, type = "hcbeta")
summary(result)
tests(result)
confint(result)
plot(result)Start with vignette("introduction", package = "hcinfer") for a compact
overview of the package API.
- White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817-838. doi:10.2307/1912934.
- Hinkley, D. V. (1977). Jackknifing in unbalanced situations. Technometrics, 19(3), 285-292. doi:10.1080/00401706.1977.10489550.
- Horn, S. D., Horn, R. A., and Duncan, D. B. (1975). Estimating heteroscedastic variances in linear models. Journal of the American Statistical Association, 70(350), 380-385. doi:10.1080/01621459.1975.10479877.
- MacKinnon, J. G. and White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29(3), 305-325. doi:10.1016/0304-4076(85)90158-7.
- Davidson, R. and MacKinnon, J. G. (1993). Estimation and Inference in Econometrics. Oxford University Press.
- Cribari-Neto, F. (2004). Asymptotic inference under heteroskedasticity of unknown form. Computational Statistics and Data Analysis, 45(2), 215-233. doi:10.1016/S0167-9473(02)00366-3.
- Cribari-Neto, F. and da Silva, W. B. (2011). A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model. AStA Advances in Statistical Analysis, 95(2), 129-146. doi:10.1007/s10182-010-0141-2.
- Cribari-Neto, F., Souza, T. C., and Vasconcellos, K. L. P. (2007). Inference under heteroskedasticity and leveraged data. Communications in Statistics - Theory and Methods, 36(10), 1877-1888. doi:10.1080/03610920601126589.
- Li, S., Zhang, N., Zhang, X., and Wang, G. (2016). A new heteroskedasticity-consistent covariance matrix estimator and inference under heteroskedasticity. Journal of Statistical Computation and Simulation, 87(1), 198-210. doi:10.1080/00949655.2016.1198906.
- Cunha, M. A., Cribari-Neto, F., and Marinho, P. R. D. (manuscript). A beta-based heteroskedasticity-consistent covariance matrix estimator.