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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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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
  • 561
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
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
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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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
  • 243
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
11 votes
1 answer
877 views

Linear regression with overlapping observations

Suppose we're doing univariate linear regression between X and Y. Let's say X are daily observations, and Y reflects how some variable changes 1 year into the future. So Y observations will be ...
rinspy's user avatar
  • 3,370
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
9 votes
0 answers
1k views

What GEE-exchangeable method can do that robust variance can't?

I asked a related question before here on the difference between GEE method with exchangeable varcov structure v. Robust standard errors known as Huber White method in group randomized trials. As ...
Sam's user avatar
  • 2,184
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
8 votes
1 answer
13k views

Formula for Newey West Standard Error

Could someone please help with the formula for the Newey West standard error of $\beta_1$ (without matrix notation) for the following regression: $Y_t=\beta_0+\beta_1X_t+\epsilon_t$ where $\...
ts_highbury'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
8 votes
1 answer
9k views

How are robust standard errors calculated in the case of logistic regression?

I mean: the Huber/White/sandwich estimator of standard errors. It seems to me that, in the case of continuous outcomes, robust estimators of standard errors are rather simple, given that variance of ...
Federico Tedeschi's user avatar
6 votes
2 answers
1k views

Getting understand HAC estimators

Can you help me please with understanding HAC estimator ? I've searched whole internet about it and I didn't find any page which explains clearly algorithm of HAC. I would also see some mathematical ...
John's user avatar
  • 532
6 votes
2 answers
354 views

Robust Variance Estimation in Bayesian Meta-analysis

BACKGROUND I am conducting a meta-analysis using the brms in R with structure akin to the following (leaving out priors, etc. ...
jmfawcet's user avatar
  • 432
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
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
  • 61
6 votes
1 answer
8k 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 ...
bsbk's user avatar
  • 1,197
6 votes
1 answer
12k views

Estimating robust standard errors in panel data regressions

I am trying to estimate robust standard errors in a panel data regression. I understand panel data regressions conceptually, but R offers a lot of options I am not sure about. My data is of the ...
EDC's user avatar
  • 388
6 votes
0 answers
531 views

Robust standard errors under overlapping observations: confusing simulation results from alternative methods

I have a time series of $h$-step-ahead forecasts $\hat{y}_{t+h|t}$ for $t=1,\dots,T-h$ with $h>1$. I also have the corresponding realized values of their targets $y_{t+h}$ and the corresponding ...
Richard Hardy's user avatar
5 votes
2 answers
371 views

Do I need to test for autocorrelation or normality assumption if I am running the regression with standard errors?

I used OLS regression to estimate a relationship between X and Y with a couple of control variables. However, when I tested for heteroskedasticity with Breusch–Pagan/Cook–Weisberg test, my residuals ...
Laiy's user avatar
  • 245
5 votes
1 answer
573 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
5 votes
2 answers
448 views

Conditions for validity of a robust-error-variance Poisson regression

A variant of a Poisson regression called the "robust-error-variance Poisson regression" is an approach adapted for binary data, specially as an alternative to the logistic regression. What are the ...
Happy Cretine's user avatar
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
5 votes
1 answer
4k views

OLS regression - robust estimates for parameter's variance

I'm estimating a model for corporate social responsibility (not important). I have found my variable of interest significant at 5% confidence level. My sample is $N=84$, cross-section. For this I ...
Jose Sanchez's user avatar
5 votes
1 answer
819 views

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
  • 63k
5 votes
1 answer
2k views

When to use Newey West vs. GLS? [duplicate]

Suppose I run OLS regression and find my residuals to be autocorrelated. When should I use a procedure like Newey-West and when should I use GLS modelling, ie. specifying some ARMA structure to the ...
badmax's user avatar
  • 2,211
5 votes
0 answers
1k views

HAC standard errors: small-sample correction

The Python package statsmodels provides a use_correction option when computing HAC standard errors for an OLS model, which ...
Anthony's user avatar
  • 520
4 votes
3 answers
2k views

Do I get the heteroskedasticity-robust standard errors from my OLS or WLS regression?

I have a multiple regression linear model which I ran a simple OLS test on. I then performed the White test and found that it was heteroskedastic. Then I performed a Weighted Least Squares regression ...
Jona's user avatar
  • 273
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
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
  • 705
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
  • 1,140
4 votes
1 answer
4k views

How to add robust error variances in glm Poisson model in R? [closed]

I am running two parallel analyses for log poisson regression in R and State. In Stata, there is an option of specifying "robust" within the code, but within the R code: ...
power_of_epi's user avatar
4 votes
1 answer
524 views

Robust Variance in Stata and R?

There are already a lot of good questions on this topic (e.g., here). But they address complexities that I am not interested in. I have some simple data. I am using basic GLM and OLS, with robust ...
Ashley Naimi's user avatar
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
  • 1,568
4 votes
1 answer
1k views

Survival analysis: Frailty vs Sandwich variance estimators

Question Can someone answer (in as non-technical terms as possible) whether or not frailty models and robust sandwich variance estimators are trying to solve the same problem in different contexts? ...
fen's user avatar
  • 87
4 votes
1 answer
895 views

Assumptions of path analysis when multivariate normal distribution is violated

I'm creating my first path analysis model with lavaan (R package). The assumption of multivariate normal distribution, however, is violated. Also, in the regression M1 ~ X1 + X2 (mediator ~ exogenous ...
Rizzi's user avatar
  • 95
4 votes
1 answer
2k views

How to compute robust standard errors of the coefficients in multiple regression?

So I know that to find the coefficients of the BLP of some data is to use the formula, $$\vec{\beta} = [{\bf X}^{T}{\bf X}]^{-1}{\bf X}^{T}{\bf Y}.$$ However, I also want to find the variance, and I ...
Addem's user avatar
  • 489
4 votes
0 answers
1k views

When are heteroscedasticity-robust (Huber-White's) standard errors useful and when are they not? [closed]

Short version Considering the controversy regarding this practice and having learn that heteroscedasticity should be addressed differently, I wondered: In which cases should one consider computing ...
Fanfoué's user avatar
  • 641
4 votes
0 answers
630 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
  • 508
4 votes
0 answers
911 views

Robust regression for autocorrelation and heteroskedasticity - coefficients do not change, only standard errors change?

When using Newey-West robust standard errors to deal with heteroskedasticity and autocorrelation: http://support.sas.com/kb/40/098.html is it correct to state that the coefficients are not different ...
adrCoder's user avatar
  • 483
4 votes
0 answers
937 views

Correcting for heteroscedasticity in logistic regression

I am using a large health dataset as a part of a research project (N = ~18 000). My colleagues and I are investigating whether smoking predicts the presence or absence of a mental illness. We are ...
user80000's user avatar
3 votes
3 answers
3k views

Inverse Probability Weighting and Robust Estimation

The example is from https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/. Chapter 12. In causal inference, it is common to get inverse probability weighting then fit the weighted ...
failedstatistician's user avatar
3 votes
2 answers
17k views

Robust standard errors in multiple regression

I use Andrew F. Hayes' macro for SPSS (HCREG at http://www.afhayes.com/spss-sas-and-mplus-macros-and-code.html) to perform multiple regression analyses with robust standard errors. The information I ...
Bonnie's user avatar
  • 31
3 votes
2 answers
171 views

Distribution of medians of triplicate samples taken from Gaussian distribution

My Monte Carlo simulation seems to show that the standard deviation of the medians of triplicate samples taken from the Gaussian distribution approaches 2/3 of the SD of the original distribution. ...
Maciej Tomczak's user avatar
3 votes
1 answer
132 views

How does Huber compute the $\operatorname{var}(s_n)/E[s_n]^2$ and $\operatorname{var}(d_n)/E[d_n]^2$?

(N.B. I am cross posting this question from math stackexchange since after x days I have still not received any responses.) How does Huber in book 'Robust statistical procedures' in chapter 1 compute ...
peter's user avatar
  • 31
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
  • 63k
3 votes
1 answer
2k views

Robust standard error in generalized least squares regression

Suppose we have a correlated outcome $\mathbf{y}$ and a bunch of predictors $\mathbf{X}$. For some reason, we know the variance/covariance matrix of the error term $(\epsilon)$, say $\mathbf{V}$. In ...
boscovich's user avatar
  • 1,676
3 votes
2 answers
1k 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 ...
bayes003's user avatar
  • 369

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