Questions tagged [sandwich]

Sandwich, or sandwich variance estimation, refers to a method of estimating standard errors from estimating equations that is robust to many model based assumptions. The preferred tag is "robust-standard-error"

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r quantreg - quantile regression with clustered standard errors

I fit a quantile regression using quantreg:::rq on clustered data. I use the Huber sandwich estimator to obtain cluster-corrected standard errors, which is ...
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2k views

Sandwich Estimator in Maximum Likelihood Estimation of Logit

I am estimating a discrete choice model using mixed logit using Halton Draws. So everything is effectively done with MCMC. The code is written in MATLAB. I am using MATLAB's ...
796 views

How to calculate robust standard error with offset?

I think the default vcovHC in R's sandwich package does not handle offsets in Poisson models. We see this because robust (...
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173 views

Calculating sandwich estimator

Considering design matrix $X \in \mathbb{R}^{n\times p}$ $(n>p)$ and response $y\in \mathbb{R}^{n}$. The sandwich estimator can be calculated directly using (X^TX)^{-1}X^T diag(r^2) X (X^TX)^{-...
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829 views

Multinomial tests of heterogeneity using robust standard errors?

I am looking at differences in frequencies of categorical variables collected at different sites, adjusting for stratification factors of age and sex. I would like to use robust standard error ...
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219 views

Why sandwich estimators aren't always used in OLS regression?

I asked before what is the intuition behind sandwich estimators. I must still missing something because I don't understand why sandwich estimators are not always applied to OLS residuals. Can you ...
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618 views

Do robust standard errors protect you from proportional odds assumptions?

Cox Proportional Hazards models are traditionally taught alongside proportional hazards assumptions. There is a corresponding test of proportionality. However, if standard errors are calculated from ...
• 63.5k
1 vote
117 views

If the normality assumption in the for the GLS estimation fails, would you switch to GEE?

I want a marginal model, ideally fit via GLS. But the normality of residuals doesn't hold. It isn't much skewed, I don't want any transformations. It's just non-normal in shape. Yet still reporting ...
1 vote
85 views

M-estimator: There is no "of something" in the definition

I see that when talking about estimator, we have "of something", where "something" refers to a fixed parameter. For example, we say that the sample mean is an estimator of the ...
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1 vote
128 views

Why cannot the sandwich SE be used when the Kenward-Roger denominator df are in use?

This question is about mixed models, and the use of both the Kenward-Roger correction (K-R) for small sample sizes and robust (sandwich) estimators for standard errors in the same model. Is it ...
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1 vote
450 views

Robust regression with Sandwich estimator

I understand that rlm (robust regression) addresses issues of outliers and influential observations, but does not address heteroskedasticity. I have come to learn ...
• 715
1 vote
941 views

Different optimal bandwidths of Newey West (1994) in R and STATA

R and STATA gave very different optimal bandwidths for the same data set. It will be greatly appreciated if someone can give me any hint why this happens. Here are two sample codes from R and STATA ...
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1 vote
77 views

I can't correct the OLS model with heteroskedasticity by the lmtest means

i'm using a selvaggio model to explain the behavior of deposits in a bank's data, and i need to use the estimated parameters, the problem is the heteroskedasticity that i detectect with breusch-pagan ...
1 vote
352 views

Application of Huber-White Variance Estimates in GLMER

I'm currently working on an analysis in R using GLMER mixed-effects model with a logistic regression framework under the lme4 package. I would like to include empirical (Huber–White sandwich) variance ...
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13 views

QMLE and Confidence Intervals

In the setting of Huber's paper, the true density is $y\sim f_0$, and the density under consideration is $y\sim f(\cdot|\theta)$. If we choose to maximize $y\sim f(\cdot|\theta)$, we will find that \$\...
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