Questions tagged [robust-standard-error]

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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47
votes
4answers
55k 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 ...
23
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1answer
12k 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 ...
21
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6answers
55k 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 ...
15
votes
1answer
3k 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? ...
11
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3answers
16k 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 ...
11
votes
2answers
6k 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 ...
8
votes
2answers
8k 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 ...
8
votes
1answer
10k 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 $\...
8
votes
0answers
794 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 ...
6
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1answer
6k 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 ...
6
votes
1answer
590 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} = ...
6
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1answer
4k 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-...
6
votes
1answer
9k 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 ...
5
votes
2answers
109 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 ...
5
votes
1answer
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 ...
5
votes
1answer
4k 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 ...
5
votes
1answer
5k 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 ...
4
votes
2answers
296 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 ...
4
votes
1answer
347 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} | \...
4
votes
1answer
3k 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 ...
4
votes
1answer
2k 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 ...
4
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1answer
151 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 ...
3
votes
1answer
8k 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 ...
3
votes
1answer
9k 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 ...
3
votes
2answers
16k 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 ...
3
votes
1answer
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 ...
3
votes
3answers
2k views

Checking for normality with robust errors

I am running a linear regression (just a single IV) and have selected the robust error option (vce robust) in Stata due to heteroscedasticity (and because it is ...
3
votes
1answer
135 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 ...
3
votes
3answers
1k 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 ...
3
votes
1answer
99 views

How do we obtain p values from a robust mixed regression model in R?

I have yet to find an answer to this problem, so here goes. I am fitting a robust mixed regression model using the robustlmm package on R. Unlike the lme4 package, from which we can obtain p values ...
3
votes
1answer
186 views

Address unequal variance between groups before applying contrasts for a linear model? (r)

My Goal: I have an ordinal factor variable (5 levels) to which I would like to apply contrasts to test for a linear trend. However, the factor groups have heterogeneity of variance. What I've done: ...
3
votes
1answer
363 views

Implement VAR model in R with HAC corrected standard errors [closed]

I have fitted a VAR model in R (with function VAR) and would like to use HAC corrected standard errors. How is that possible?
3
votes
1answer
6k 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.
3
votes
1answer
959 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 ...
3
votes
1answer
376 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 ...
3
votes
2answers
3k 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?
3
votes
1answer
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 ...
3
votes
0answers
47 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 ...
3
votes
0answers
40 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 ...
3
votes
0answers
333 views

Should instruments always weakly increase standard errors?

In OLS, are standard error estimates using some instrument $z$, different from $x$ always weakly larger than standard errors when not using instruments? This has been discussed before: Why is the ...
3
votes
0answers
809 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 ...
2
votes
1answer
758 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 ...
2
votes
1answer
43 views

Heteroskedasticity-Robust Standard Errors in Median Regressions

Does anyone know how to compute heteroskedasticity-robust standard errors in median regressions in R? Assume the following example: ...
2
votes
1answer
141 views

Heteroskedasticity-consistent standard errors

See https://en.wikipedia.org/wiki/Heteroscedasticity-consistent_standard_errors. Assume the model of interest is the linear regression model. If the errors are heteroskedastic, $\hat{\sigma}^2_i = \...
2
votes
1answer
345 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-...
2
votes
2answers
384 views

Robust Variance Estimate with Cox model in Complex Survey Setting

I have a single-stage cluster sample, and I am trying to estimate the hazard ratio of a given exposure after controlling for a confounder. My dataset contains 10 strata, with 20 clusters within each ...
2
votes
1answer
2k views

How to estimate a fixed effects regression WITH robust standard errors AND instrument variables

I have been trying to estimated the stated problem, but I only succeed in parts of it. The following artificial setup is supposed to illustrate my problem in detail: Setup the data: ...
2
votes
1answer
145 views

Does robust regression affect contributions to explained variance by the different variables?

I've learned that in multiple linear regresion, parameter estimates as well as R$^2$ are not affected by using robust standard errors, i.e. are the same as resulting from non-robust regression. I now ...
2
votes
1answer
900 views

Robust standard errors for cross-sectional data: what is a “large” sample size?

I know that others have asked about robust standard errors (Robust standard errors in econometrics and Always Report Robust (White) Standard Errors?). An answer to the latter question made this ...
2
votes
1answer
33 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}...