Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model
نویسندگان
چکیده
منابع مشابه
Correcting for Heteroscedasticity with Heteroscedasticity Consistent Standard Errors in the Linear Regression Model: Small Sample Considerations
In the presence of heteroscedasticity, OLS estimates are unbiased, but the usual tests of significance are inconsistent. However, tests based on a heteroscedasticity consistent covariance matrix (HCCM) are consistent. While most applications using a HCCM appear to be based on the asymptotic version of the HCCM, there are three additional, relatively unknown, small sample versions of the HCCM th...
متن کاملDiagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions
Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...
متن کاملThe Generalized Regression Model and Heteroscedasticity
y = Xβ + ε, E [ε | X] = 0, (9-1) E [εε′ | X] = σ 2 = , where is a positive definite matrix. (The covariance matrix is written in the form σ 2 at several points so that we can obtain the classical model, σ 2I, as a convenient special case.) The two leading cases we will consider in detail are heteroscedasticity and autocorrelation. Disturbances are heteroscedastic when they have different varian...
متن کاملStandard errors for the retransformation problem with heteroscedasticity.
Economists often estimate models with a log-transformed dependent variable. The results from the log-transformed model are often retransformed back to the unlogged scale. Other studies have shown how to obtain consistent estimates on the original scale but have not provided variance equations for those estimates. In this paper, we derive the variance for three estimates--the conditional mean of...
متن کاملRobust Regression Methods: Achieving Small Standard Errors When There Is Heteroscedasticity
A serious practical problem with the ordinary least squares regression estimator is that it can have a relatively large standard error when the error term is heteroscedastic, even under normality. In practical terms, power can be poor relative to other regression estimators that might be used. This article illustrates the problem and summarizes strategies for dealing with it. Included are new r...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The American Statistician
سال: 2000
ISSN: 0003-1305,1537-2731
DOI: 10.1080/00031305.2000.10474549