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"

Filter by
Sorted by
Tagged with
0
votes
0answers
11 views

Calculating robust standard errors while leaving out fixed effects in ordered logit?

I am estimating an ordered logit equation with R and trying to calculate robust standard errors. I have several thousand fixed effects in the model, (U.S. counties), and some of these coefficients are ...
1
vote
0answers
39 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 ...
1
vote
1answer
20 views

Variance Estimation for Least Squares with Probability Weights

I'm running a simulation study and finding that the nominal SEs of the estimated coefficients when using weights in lm in R are an underestimate of the simulation SE. I have confirmed that $\hat{\beta}...
0
votes
0answers
18 views

Stationarity vs. Heteroskedasticity

I am reviewing a OLS regression model based on time series data. The variables have been transformed so they are stationary; however, the existence of heteroskedasticity was assumed and the Huber ...
2
votes
0answers
36 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)^{-...
0
votes
0answers
20 views

Sandwich standard errors versus typical standard error estimation

Question: do sandwich estimators of the standard errors equal the typical estimation of the standard error IF the data was generated with constant variance in the residuals and as the sample size ...
1
vote
0answers
155 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 ...
0
votes
2answers
422 views

How to estimate robust sandwich standard errors when estimating parameters using optim() in R?

Currently I am using numerical optimization in R via the optim() function to estimate some parameters in a complicated ...
1
vote
1answer
385 views

How to calculate sandwich standard errors for generalized least squares models?

Dependent data can be modeled using covariance structures like compound symmetry, spherical, AR-1, and other. Using generalized least squares, inference can be made on the regression coefficients ...
0
votes
1answer
1k views

Wondering what type of covariance correction for standard errors is better: Hansen-Hodrick or Newey-West?

I am wondering what type of covariance correction for standard errors is better: Hansen-Hodrick or Newey-West? Also, does someone know if StatsModels package that ...
0
votes
0answers
351 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 ...
3
votes
1answer
3k views

Standard Errors with Weighted Least Squares Regression

For OLS, $\hat{\beta} = (X'X)^{-1}X'y$, and $\text{var}(\hat{\beta}) = (X'X)^{-1} X' \sigma^2 I X (X'X)^{-1}$. I can reproduce these "by hand". For WLS, with heteroskedastic errors and weights in ...
1
vote
1answer
536 views

What does setting vcov=sandwich result in? I was expecting that it will result in HC consistent standard errors [closed]

However, when I checked the results using, vcovHC=sandwich, I found different results. Can anyone help me to figure out the difference? ...
1
vote
0answers
378 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 ...
4
votes
1answer
331 views

Difference between Quasi-Poisson and Sandwich Covariance

I understand that both methods can be utilized to obtain correct inference in overdispersed Poisson data. What I don't understand is the difference between them: why the analyst would choose one over ...
4
votes
1answer
203 views

How to implement the sandwich estimator in a semi-parametric situation?

I am trying to implement a sandwich estimator described in Zhang et al. (2012, p. 1012) in very brief terms. The information they give is not enough for me to understand what has been actually done, ...
0
votes
1answer
76 views

Reference for incremental sandwich covariance from biglm?

I am working on some similar methods to Lumley's biglm wrapper around Miller's AS274 algorithm, and I can't seem to find a reference for his incremental Huber/White ...
1
vote
0answers
74 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 ...
3
votes
1answer
1k views

Clustered (grouped) standard errors MLE in R

I'm doing the following maximum likelihood estimation using mle2 function from bbmle package: ...
1
vote
0answers
173 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 ...
2
votes
1answer
96 views

effect estimate without constant variance (r sandwich vcovHC)

For the sandwich package in R, when using vcovHC(model) where model <- lm(Y~X), what gives the effect estimate regarding the ...
2
votes
0answers
1k 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 ...
2
votes
0answers
567 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 (heteroscedasticity consistent) standard ...
2
votes
0answers
144 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 ...
3
votes
1answer
2k views

Robust OLS versus ML with sandwich estimator

If you compare the standard errors of the OLS coefficients with the White correction, versus the ML estimates with the variance estimated with the sandwich estimator, which standard errors do you ...
1
vote
0answers
535 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 ...
1
vote
2answers
665 views

Clustered data WITHOUT multilevel / GEE model?

I have a data-set with around 700 observations from 12 centres. Although the clustering effect as tested in a random intercept model didn't seem significant, it seems more appropriate to use a ...
0
votes
1answer
325 views

Huber sandwich estimator in quantile regression

I need the description of Huber sandwich estimate method for quantile regression. I found this "a Huber sandwich estimate using a local estimate of the sparsity function". Sparsity function looks ...
6
votes
1answer
2k views

Pairwise comparisons for a regression with sandwich estimates (in R)

The question in short I run a regression in R and made a boxplot of the response variable with grouping by one of the predictor variables. On this boxplot I'd like to add some information about the ...
10
votes
2answers
9k views

Robust regression inference and Sandwich estimators

Can you give me an example of the use of sandwich estimators in order to perform robust regression inference? I can see the example in ?sandwich, but I don't quite ...
4
votes
1answer
638 views

Robust Residual standard error (in R)

I have a question regarding to the concept of robust standard errors. What I found about that topic is, that one can estimate the robust standard error for regression coefficients to eliminate ...