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Questions tagged [robust-standard-error]

Use this tag for questions related to any kind of robust standard error estimation, including but not limited to clusters-robust, heteroscedasticity/autocorrelation-robust, and related standard errors.

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Robust error estimation and hazard ratio with non-proportional hazards

I recall having heard that the hazard ratio, estimated in a Cox model, can be made robust against the parallel hazard functions assumption. The key to this is using a Huber-White, or Huber-Eicker-...
AdamO's user avatar
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26 votes
6 answers
71k views

Always Report Robust (White) Standard Errors?

It has been suggested by Angrist and Pischke that Robust (i.e. robust to heteroskedasticity or unequal variances) Standard Errors are reported as a matter of course rather than testing for it. Two ...
Graham Cookson's user avatar
56 votes
4 answers
69k views

Replicating Stata's "robust" option in R

I have been trying to replicate the results of the Stata option robust in R. I have used the rlm command form the MASS package ...
user56579's user avatar
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33 votes
1 answer
21k views

Sandwich estimator intuition

Wikipedia and the R sandwich package vignette give good information about the assumptions supporting OLS coefficient standard errors and the mathematical background of the sandwich estimators. I'm ...
Robert Kubrick's user avatar
13 votes
2 answers
9k views

How to get ANOVA table with robust standard errors?

I am running a pooled OLS regression using the plm package in R. Though, my question is more about basic statistics, so I try posting it here first ;) Since my regression results yield ...
Aki's user avatar
  • 517
5 votes
1 answer
2k views

Eicker-Huber-White Robust Variance Estimator

In a regression context, $$ Y_i = \alpha + \beta T_i + \varepsilon_i $$ my textbook defines EHW robust variance estimator as $$ \widehat{\mathbb{V}_{\rm EHW}}(\widehat{\alpha}, \widehat{\beta} | \...
user2978524's user avatar
8 votes
3 answers
2k views

Can robust standard errors be less than those from normal OLS?

I'm reading about Robust Standard Error Estimators for Panel Models from the developer of plm R package (Millo, 2017: 21). But my question is not about software. In ...
garej's user avatar
  • 359
6 votes
1 answer
1k views

What are the leverage values for Ridge regression?

In linear least squares the parameter estimates are: $\hat{\beta} = \left(X^{\top}X\right)^{-1}X^{\top}y$. In Ridge regression the standardized parameter estimates are given by $\hat{\beta}_{\Gamma} = ...
José Bayoán Santiago Calderón's user avatar
3 votes
1 answer
2k 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 ...
AdamO's user avatar
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17 votes
1 answer
5k views

Comparison between Newey-West (1987) and Hansen-Hodrick (1980)

Question: What are the main differences and similarities between using Newey-West (1987) and Hansen-Hodrick (1980) standard errors? In which situations should one of these be preferred over the other? ...
Candamir's user avatar
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3 votes
2 answers
128 views

Large increase in joint significance when introducing robust covariance matrix estimator

I have a linear regression model where $Y$ is regressed on five dummies $X_3,X_4,X_5,X_{10},X_{20}$. (In R, I actually regress $Y$ on a factor $X$ that produces the 5 dummies.) My sample size is 24 (...
Richard Hardy's user avatar
1 vote
3 answers
5k views

How to use an optimization solver to get t-stats and p-values for the estimates?

I calculate a data log likelihood (evaluated at a set of parameters to be estimated), and my task is to find the set of parameters that maximize my log likelihood. My problem is: thought there are a ...
Ruby's user avatar
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1 vote
1 answer
869 views

OLS + HAC std err vs. conditional mean equation from GARCH

I have two questions regarding the efficiency gain using GARCH modeling campared to OLS with HAC standard errors. If we compare the coefficient estimates from a regression using OLS and that from the ...
user100525's user avatar
13 votes
3 answers
22k views

What are the consequences of having non-constant variance in the error terms in linear regression?

One of the assumptions of linear regression is that there should be a constant variance in the error terms and that the confidence intervals and hypothesis tests associated with the model rely on this ...
Kira's user avatar
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9 votes
2 answers
10k views

Using HAC standard errors although there might be no autocorrelation

I'm running a couple of regressions and, as I wanted to be on the safe side, decided to use HAC (heteroskedasticity & autocorrelation consistent) standard errors throughout. There might be a few ...
Juliett Bravo's user avatar
8 votes
1 answer
11k views

cluster-robust standard errors are smaller than unclustered ones in fgls with cluster fixed effects

I'm currently working on some experimental data. The experimental design consists of two treatments. In each treatment, 20 subjects are randomly matched in pairs and participate to a simple game. The ...
sciacallojo's user avatar
6 votes
1 answer
6k views

F-test formula under robust standard error

I am attempting to write a program that will (among other things) use the F-test in multivariate regression under standard robust errors. I am having trouble finding a specific formula for the F-...
Alex's user avatar
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5 votes
1 answer
581 views

Robust standard errors for a Poisson regression with/without an offset

The following is a question posted to Stack Overflow, but the answer is more to-do with statistical theory than software. I am reposting it here because this is a more appropriate venue, and because ...
Demetri Pananos's user avatar
5 votes
1 answer
2k views

Newey-West standard errors when Durbin-Watson test results are fine

I am running a time-series regression. The Durbin-Watson statistics is very close to 2. In such a situation, would it still be better to use Newey-West standard errors, or is it ok to use OLS standard ...
financial theory's user avatar
4 votes
0 answers
637 views

Correcting for robust/clustered standard errors within the lm function or replacing the results

Cross posted on Stackoverflow with a bounty of 200. EDIT: I think I have to clarify this question a little bit more. So what I am looking for, is a function in which I can provide both the vcov matrix ...
Tom's user avatar
  • 528
4 votes
1 answer
12k views

Heteroskedasticity removed through fixed effect estimation?

I have a large panel data set. Examination of a pooled OLS regression with Breusch Pagan showed heteroskedasticity with all model specifications. I consequently chose to use panel-corrected standard ...
altabq's user avatar
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4 votes
1 answer
12k views

Adjusted $R^2$ & F test are not shown in regression with robust standard errors in Stata

The adjusted $R^2$ is not shown when a regression with robust standard errors is calculated in Stata. This is surprising to me since the value of the $R^2$ is unaffected in regressions with robust ...
Cesare Camestre's user avatar
4 votes
1 answer
3k views

Does the sandwich estimator in GEE protect against both correlation misspecification and heteroscedasticity?

The relative merits of GEE with exchangeable correlation or GEE with independence and the sandwich estimate have been discussed, but I couldn't find a post specifically addressing my question. I have ...
Moose's user avatar
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4 votes
1 answer
3k views

Mixed effects negative binomial with robust standard errors (Huber-white) in R

I would like to fit a random effects model in R using the negative binomial distribution and reporting robust standard errors. I was going to try using the sandwich package to compute the robust ...
Doug Fir's user avatar
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3 votes
1 answer
8k views

How to calculate the robust standard error of predicted y from a linear regression model in R? [closed]

How can I calculate the robust standard error of the predicted y from a linear regression model in R? Any suggestion is appreciated.
user53154's user avatar
3 votes
2 answers
5k views

Newey-West robust standard errors for autocorrelation only (no heteroskedasticity)

May I use the Newey-West procedure when I have only autocorrelation? Or can I only use the Newey-West when I have autocorrelation and heteroscedasticity?
D. Adams's user avatar
3 votes
1 answer
61 views

Are there formal measures for classifier or regression robustness?

Are there performance measures that produce a numerical value of the robustness of a classifier or regression. By robustness I mean graceful degradation in performance to unexpected input (similar to ...
user1172468's user avatar
  • 2,035
3 votes
0 answers
302 views

Are HAC robust standard errors robust against autoregressive conditional heteroskedasticity?

Suppose I have a GARCH(p,q) model with constant conditional mean, \begin{aligned} y_t &= \mu + u_t, \\ u_t &= \sigma_t \varepsilon_t, \\ \sigma_t^2 &= \omega + \alpha_1 u_{t-1}^2 + \dotsc +...
Richard Hardy's user avatar
3 votes
1 answer
208 views

Recommend monograph on statistical model misspecification

Is there a good book on statistical model misspecification in general? It should cover, for example, the behavior of estimators (e.g., maximum likelihood) when the specified parametric family does not ...
Uchiha's user avatar
  • 133
3 votes
0 answers
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 (...
AdamO's user avatar
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2 votes
1 answer
559 views

Robust SE clustered GLM Gamma Log Link to match GEE Robust SE

How do I get the robust standard errors/sandwich variance estimators for GLM using a Gamma family with a log-link to match the robust standard errors from the GEE output? ...
renethestudent's user avatar
2 votes
0 answers
2k views

Is delta method better than bootstrap to generate standard error for marginal effects?

I read here, here, here, here, and elsewhere that " Parametric bootstrap closely related to objective Bayes. (That’s why it’s a good importance sampling choice.) When it applies, parboot approach ...
Krantz's user avatar
  • 585
2 votes
1 answer
9k views

Clustered standard errors and robust standard errors

I was wondering if, when running a regression on panel data, clustered standard errors are already correcting for heteroskedasticity. Actually, I have run such a regression and detected ...
Phil's user avatar
  • 23
1 vote
0 answers
68 views

For repeated data, why we don't use just OLS with sandwich SEs but rather GLS or mixed models?

If I have repeated observations and want to summarize the means at each time point, the OLS will give me the true, raw means, while the GLS will give me means dependent on the selected covariance ...
Blitzkordk's user avatar
1 vote
1 answer
37 views

Heteroscedasticity-consistent (robust) standard errors complemented by i) confidence intervals for beta, ii) Tolerance and iii) VIF values in R?

In order to solve heteroscedasticity in my data, I ran a regression with heteroscedasticity-consistent ("robust") standard errors. I would also like to report i) standardized betas together ...
mbp's user avatar
  • 11
1 vote
1 answer
2k views

Double-clustered standard errors and large panel

I have a large panel data set featuring the purchases of 5000+ individuals over 2000+ time periods (days). I am looking to estimate pooled OLS regressions featuring double-clustered standard errors (...
Andrew Parkerson's user avatar
1 vote
1 answer
2k 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 ...
JorgeT's user avatar
  • 351
0 votes
1 answer
99 views

What is the effect of robust estimators of covariance variance (Newey-West) on the VAR model?

What will change in VAR model if I will introduce robust estimators of covariance variance (Newey-West)? Will only the interpretations change and the properties of the model remain the same? Or maybe ...
kasandraaa's user avatar