What does the robust command in Stata do?
robust is a programmer’s command that computes a robust variance estimator based on varlist of equation-level scores and a covariance matrix. robust helps implement estimation commands and is rarely used. That is because other commands are implemented in terms of it and are easier and more convenient to use.
What does _B do in Stata?
Post-estimation commands in Stata _b[] for categorical variables. ORIGINAL: A post estimation command can be used to predict the value of the dependent variable. Here is an example, where you can type _b[_cons] + _b[x1]*1 + _b[x2] to get an actual value of Y.
What is Nestreg in Stata?
nestreg fits nested models by sequentially adding blocks of variables and then reports comparison tests between the nested models. Quick start. Fit nested (hierarchical) models sequentially, including covariates x1 and x2 first and then adding x3.
What does robust regression mean in Stata?
Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. …
Can robust standard errors be smaller?
The lesson we can take a away from this is that robust standard errors are no panacea. They can be smaller than OLS standard errors for two reasons: the small sample bias we have discussed, and the higher sampling variance of these standard errors.
What happens if there is Heteroskedasticity?
Heteroscedasticity tends to produce p-values that are smaller than they should be. This effect occurs because heteroscedasticity increases the variance of the coefficient estimates but the OLS procedure does not detect this increase.
What does R mean in Stata?
return list lists results stored in r(). ereturn list lists results stored in e().
What is Lincom in Stata?
Description. lincom is a postestimation command for use after sem, gsem, and nearly all Stata estimation commands. lincom computes point estimates, standard errors, z statistics, p-values, and confidence intervals for linear combinations of the estimated parameters.
What is a hierarchical regression?
A hierarchical linear regression is a special form of a multiple linear regression analysis in which more variables are added to the model in separate steps called “blocks.” This is often done to statistically “control” for certain variables, to see whether adding variables significantly improves a model’s ability to …
What is robust standard error?
“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. “Robust” standard errors have many labels that essentially refer all the same thing. Namely, standard errors that are computed with the sandwich estimator of variance.
What is robust regression analysis?
In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods. Regression analysis seeks to find the relationship between one or more independent variables and a dependent variable.
Does Heteroskedasticity increase standard error?
Heteroscedasticity does not cause ordinary least squares coefficient estimates to be biased, although it can cause ordinary least squares estimates of the variance (and, thus, standard errors) of the coefficients to be biased, possibly above or below the true of population variance.
How is a robust regression calculated in Stata?
Stata’s rreg command implements a version of robust regression. It first runs the OLS regression, gets the Cook’s D for each observation, and then drops any observation with Cook’s distance greater than 1. Then iteration process begins in which weights are calculated based on absolute residuals.
When to use robust regression instead of least squares regression?
Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations.
When to use Cook’s distance in robust regression?
Influence can be thought of as the product of leverage and outlierness. Cook’s distance (or Cook’s D): A measure that combines the information of leverage and residual of the observation. Robust regression can be used in any situation in which you would use least squares regression.
What does an outlier mean in Stata regression?
An outlier may indicate a sample peculiarity or may indicate a data entry error or other problem. Leverage : An observation with an extreme value on a predictor variable is a point with high leverage.