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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2
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0answers
715 views

Double clustered standard errors and degrees of freedom in Wald-style F test for joint significance of regression coefficients with panel data

In panel regression, the Wald-style F test for joint significance of the regression coefficients is usually done with an adjustment for the degrees of freedom when robust/clustered standard errors are ...
1
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0answers
104 views

heteroskedasticity and out-of-sample predictions

My model is an OLS with a single independent variable on cross-sectional data (n=3500). The relation is linear and there's a very good fit and there are no outliers, but the residuals' variance ...
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0answers
357 views

Robust clustering of standard errors mixed autoregressive models

I have a panel data of the format | age.months | app_id | rank_category | promoted | avg_monthly_rating |... I am using xtmixed where level 2 is app_id. My ...
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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?
8
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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 ...
2
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1answer
411 views

Negative binomial regression in presence of autocorrelation

I am running a negative binomial regression on frequency data. My y variable has very material autocorrelation at lag 1,2. Am I correct in that the error estimate on any estimated parameter under ...
2
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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
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1answer
360 views

R- is it safe to use the package sandwich for instrumental variable or GMM models?

Both of the following approaches should lead to the same results in my opinion. This is a modified example from ?ivreg where I wanted to use ...
1
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0answers
201 views

If I do a robust regression using standard error, what do I need to analyse in the residuals

Let's say I do a multiple regression, using robust (Stata option). It is a robust standard error regression. I want to analyse and discuss residuals. Residuals versus fitted values Is it sufficient ...
3
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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 ...
1
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1answer
941 views

Multinomial logit, standard and robust errors

I'm computing a multinomial logit model with Biogeme. Looking at the final results, it seems that some variables are not statistically significant when standard errors are considered. However, with ...
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0answers
119 views

Measuring robustness of network constructed with python mapper

I am trying to visualize a large multidimensional data set with the help of the Python Mapper (open source software package using the Mapper-Algorithm, a method of Topological Data Analysis). http:/...
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0answers
242 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
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0answers
584 views

Comparing Classical and Robust (Huber-White/sandwich/heteroscedasticity consistent) Standard Errors in Linear Multiple Regression

I'm running a linear multiple regression model of the type $y_i = \beta_0 + \beta_1 X_{i1} + \beta_2 X_{i2} + \beta_3 X_{i3} + u_i$. I came across King and Roberts' 2015 paper called "How Robust ...
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0answers
336 views

Estimating Studentized Residuals (or Another Similar Measure) After Linear Regression With Robust Standard Errors

I have estimated a linear multiple regression with robust standard errors using Stata (regress depvar indepvar1 indepvar2 indepvar3 indepvar4 indepvar5, robust). ...
0
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1answer
131 views

CSTS dataset - robust standard errors lead to big drop in t-values

I am running a regression on a Cross section time series data set (cross sectional dominant) that has the following characteristics: 1,200 cross sections (6 countries * 200 products). Each country - ...
1
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0answers
94 views

Constant treatment variable: Downwards bias of standard errors

I'm reading a paper that claims The treatment variable is constant within each state. This causes the errors to be biased downwards. However, the claim is not referenced, and I cant force my (weak)...
0
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1answer
393 views

SE robust to autocorrelation and testing afterwards

I was running a 2SLS regression with ivreg2 in Stata and I used the Cumby and Huizinga's test for autocorrelation with ivactest -...
1
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2answers
222 views

Panel Data, Fixed Effects regression

I have a question about the fixed effects model. I have 25 manufacturing sectors for a 12 year period (balanced panel data set) and I try to find out the effect of each sector's investment ...
3
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0answers
806 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 ...
3
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1answer
134 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 ...
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0answers
194 views

Why does a robust linear model fitting give a residual standard error?

The way I understood when to use a a robust linear fitting is for example when your variance is not constant (e.g. when you have heteroscedasticity as shown with a Breusch-Pagan test for example) or ...
1
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3answers
4k 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 ...
6
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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
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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 ...
1
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3answers
722 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 ...
2
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1answer
144 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 ...
11
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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 ...
3
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1answer
1k 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 ...
1
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1answer
2k views

Example of multi-way clustering robust standard errors: puzzling results

I am trying to get a grasp on Cameron, Gelbach and Miller (2011) robust inference with multiway clustering. As I understand, bottom line is that ignoring clustering may result in standard errors ...
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 ...
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0answers
326 views

Prediction interval using predict and NeweyWest in R

I have a basic linear regression model I fitted to a time series. Unfortunately I have to account for autocorrelation and heteroskedasicity in the model and I have done so with the NeweyWest function ...
46
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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 ...
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0answers
321 views

Robust multivariate Wald test for significance in proportional odds model

I am using the rms package (Harrell) to estimate a proportional odds model to determine the association between an ordinal outcome (frequency of pain) and the ...
3
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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.
2
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0answers
179 views

Poisson regression on binary outcomes with `glmnet`? [closed]

Because my event is not particularly rare, I am more interested in risk ratios than odds ratios for my current generalized linear modeling problem, which calls for regularized regression. I'm doing ...
4
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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 ...
1
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1answer
5k 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 ...
3
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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 ...
2
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1answer
893 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 ...
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-...
1
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0answers
441 views

Can a Linear-Log model be used instead of Robust Standard Errors?

If your regression model has heteroskedastic residuals, one should calculate White Standard Errors that correct for the mentioned heteroskedasticity. If the residuals are also autocorrelated one ...
4
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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 ...
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0answers
104 views

Panel data: predictor variables with different observation times/frequencies

I would like to estimate a standard logit (panel data): $\text{logit}(P(y_{i,t}=1))= \alpha + \beta_1 x^1_{i,t} + \beta_2 x^2_{i,t} +\epsilon_{i,t}$. The problem I am facing, however, is that $x^2$ ...
3
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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 ...
0
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0answers
2k views

Hettest Not Appropriate for Robust Cluster

I am using stata, and running a regression with robust standard errors. I want to test the Heteroscedasticity after running the regression with robust standard errors, and to compare this to before ...
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 ...
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0answers
736 views

Delta method in a multinomial logistic regression

I am estimating a multinomial logistic model with R (package "mlogit"). I use the estimated coefficients to get the estimated odds and then I apply the Delta Method (package "deltamethod") to get ...
3
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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 ...
8
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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 ...