QREG2: Stata module to perform quantile regression with robust standard errorsJ Santos SilvaJose A F Machado
-rreg- is a reasonably complicated routine (with some switching between objective functions as far as I can recall), and the procedure for computing standard errors is even less transparent. Technically since -rreg- is an M-estimator, one should be able to construct the analogue of -_robust...
FromDiemo Urbig <urbig@econ.mpg.de> Tostatalist@hsphsun2.harvard.edu Subjectst: robust regression with robust errors DateMon, 17 Nov 2008 12:03:53 +0100 Follow-Ups: Re: st: robust regression with robust errors From:"Stas Kolenikov" ...
where the “latent” explanatory variableξ is not directly observed (hence the Greek letter); x2 is directly observed and measured without error; and the regression error ɛ behaves according to the usual assumptions—in particular, E(ɛi) = 0, and the errors are uncorrelated with each ...
If the standard errors are too small, but the coefficient estimates themselves are not affected, the t-statistics will be too large and the null hypothesis of no statistical significance is rejected too often. The opposite will be true if the standard errors are too large. ...
When results from this test are statistically significant, consult the robust coefficient standard errors and probabilities to assess the effectiveness of each explanatory variable. Regression models with statistically significant nonstationarity are often good candidates for Geographically Weighted Regression (...
Quantiles of errors: RMSE comes with a disadvantage, as it takes mean of all the data points. Assuming the dataset consists of an outlier, it will have a major impact on the average value. The effect of large outliers during evaluation can be reduced by using robust metric called quantiles...
For robust regression infitlm, set the'RobustOpts'name-value pair to'on'. Specify an appropriate upper bound model instepwiselm, such as set'Upper'to'linear'. Indicate which variables are categorical using the'CategoricalVars'name-value pair. Provide a vector with column numbers, such as[1 ...
The null hypothesis of this test is homoscedasticity. If we find heteroskedasticity, then we can adjust the standard errors by making them robust standard errors. Another test to control for heteroskedasticity is: estat hettest I suggest you to check this out because it has several interesting opti...
Robust regression provides an alternative to least squares regression that works with less restrictive assumptions. Specifically, it provides much better regression coefficient estimates when outliers are present in the data. Outliers violate the assumption of normally distributed residuals in least squares...