QT: Quasi-t test in linear regression models

In hcci: Interval Estimation of Linear Models with Heteroskedasticity

Quasi-t test in linear regression models

Description

This function performs the quasi-t test for the parameters that index linear regression models, considering models with unknown heteroscedasticity, where HC methods are used to estimate the covariance matrix.

Usage

  QT(model, significance = 0.05, hc=4, h0=0, ...)

Arguments

model	Any object of class lm;
significance	Significance level of the test. By default, the level of significance is 0.05;
hc	Method HC that will be used to estimate the covariance structure. The argument method may be 0, 2, 3, 4 or 5;
h0	Constant used in the null hypothesis (default is h0 = 0);
...	Additional arguments to be passed to the function HC.

Value

Returns an object of class list with the estimated covariance matrix.

Author(s)

Pedro Rafael Diniz Marinho <[e-mail]>

References

Cribari-Neto, F. (2004). Asymptotic inference under heteroskedasticity of unknown form. Computational Statistics and Data Analysis, 45, 215-233.

Examples

  library(hcci)
  data(schools)
  y = schools$Expenditure # dependent variable
  x = schools$Income/10000 # regressor scaled by 10^4
  model_1 = lm(y ~ x)
  model_2 = lm(y ~ x+I(x^2))
  QT(model_1, significance = 0.05, hc=4, h0=0)
  QT(model_2, significance = 0.05, hc=4, h0=0)

hcci documentation built on April 12, 2025, 1:52 a.m.