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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28 views

Correcting for bias in GEE models with small cluster size

In GEE, several methods have been proposed for correcting for bias when the cluster size is small to moderate (<40). Some have proposed alternative variance estimators, e.g. Morel, Bokossa, and ...
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4 views

ARL0 and ARL1 EWMA control chart for simulation skewed data in R

I encounter problems when using r programming to run my code. In my study, I use robust estimator Sn,MAD, IQR and Biweight standard deviation for skewed data. In theory my ARL0 by using robust ...
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16 views

Is it a problem if cluster sizes vary wildly in a cluster-randomized experiment?

Consider a cluster-randomized experiment. There are 4,000 clusters and 2,000,000 observations. The dependent variable y is dichotomous, $Y \in \{0, 1\}$, and ...
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6 views

Determine ARL for EWMA using R simulation for contaiminated normal distribution(gamma distribution)

I need to do simulation using R to find the ARL( EWMA control chart )for the skewed data(i use gamma distribution for this purpose. Below are my simulation for the gamma distribution of shape 0.7 ...
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1answer
31 views

Heteroskedasticity-Consistent Covariance Matrix Estimation

I would like to ask about the difference between the vcovHC and vcov in R. The former is described as the Heteroskedasticity-Consistent Covariance Matrix Estimation. What is the difference between ...
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26 views

What is a computationally tractable way to infer statistical power in the context of OLS with robust clustered standard errors?

I have clustered data and am trying to scale up OLS with robust clustered standard errors (implemented in Python Statsmodel) as the default analytical framework going forward. It is important that I ...
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0answers
23 views

OLS question about standard errors and Heteroscedasticity

In the case of OLS, what stops one from modelling heteroscedasticity and providing predictor dependent coefficients. So, different ranges of the predictor with their own standard errors? Is this ...
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98 views

Different standard errors from ivreg and ivregress 2sls commands

In stata 15, I accidentaly used the command ivreg without specifying 2sls. In the output, it declared that 2sls was being used so I proceeded. However, later when double-checking my results I used the ...
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11 views

Estimating panel-robust standard errors for a weighted 2SLS estimator

I am trying to estimate the panel-robust standard errors of a weighted 2SLS estimator. Stata can compute. However, the documentation does not really explain how. Do you know the formula or how to ...
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336 views

Stata: options vce(robust) and vce(cluster)

I have a panel of firm data and my supervisor recommended vce(cluster firmID) for clustering the standard errors. However, the ...
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0answers
31 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)^{-...
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1answer
57 views

Robust standard errors in regression

Your task is to investigate how fertility affects labour supply. In particular, you will examine how a woman's labour supply falls when she has more than two children. Your answers should include your ...
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1answer
91 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?
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1answer
386 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 ...
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24 views

Large N Large T Unbalanced Panel - Serial Correlation

I have an unbalanced panel data, which consists of 180 vintage groups. The ith group will have time series length of t=181-i. For example, Group 1 will have 180-month observations; Group 2, originated ...
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1answer
346 views

R: Confused about robust standard errors using “felm” and “huxreg”

I’m working on panel data and want to use a “least squares dummy variable” (LSDV) approach, aka using factors to control for fixed effects rather than “within” differences. I use a large set of ...
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1answer
962 views

R | Robust standard errors in panel regression clustered at level != Group Fixed Effects

I want to run a regression on a panel data set in R, where robust standard errors are clustered at a level that is not equal to the level of fixed effects. That is, I have a firm-year panel and I want ...
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230 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 ...
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1answer
88 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 = \...
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3answers
283 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 ...
4
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1answer
171 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} | \...
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1answer
192 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 ...
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1answer
83 views

Poisson approximation of a binomial model with random effects - how to get robust variance estimates

I'm interested in using a Poisson approximation for modeling a common (~40% of the time) binary outcome. But my data has some clustering in it, having come from three different sites, which suggests ...
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0answers
439 views

Explanation and formula for Modified Z-score?

I'm struggling understanding and finding a reference for Modified (robust) z-score. I found a reference that says I have to adjust by 1.25 or 1.48 depending on the value of the median absolute ...
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66 views

robust regression coefficients [duplicate]

I ran a robust regression using the rlm package but I see that p-value of the coefficients are not returned. What is the best way to test the coefficients returned from rlm for significance? I was ...
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1answer
194 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-...
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1answer
814 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 (...
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70 views

Regressing adjusted-R2 on a constant

Lets say I have a panel of adjusted-R2 statistics from some regressions involving experiment / survey data. Can I regress these adjusted-R2 statistics on a constant to find the average adjusted-R2, ...
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0answers
27 views

Collaborator pushing for robust sandwich estimator - is it appropriate?

I have a dataset that is survival data for 3 different treatments, single time point data (ie: % survived at end of treatment), with an n=10*3 for each treatment. This data is non-parametric, which I ...
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2answers
344 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 ...
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1answer
268 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 ...
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0answers
124 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 +...
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0answers
37 views

(OLS) Two-way interaction significant but turns marginally significant using robust standard errors

I'm using American National Election Studies'(N=4,219) time series (Sept 2016-Jan 2017) data. I tested a theoretically supported interaction in a fully specified OLS model, which proved to be ...
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1answer
304 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} = ...
14
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1answer
1k 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? ...
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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 ...
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3answers
460 views

Using model with heteroskedasticity for predictions?

I am running a model with ordinary least squares regression, and am using robust standard errors (RSE's) because diagnostics tests indicated heteroskedasticity of the model. I'm a bit limited in the ...
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0answers
320 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 ...
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0answers
166 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 ...
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2answers
215 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 ...
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0answers
299 views

Why are my Robust SE in a Negative Binomial smaller than non-robust SE?

I am running a Negative Binomial Regression to predict a count variable of interest that is overdispersed and I've run into a problem. When I include robust standard errors by independent variable of ...
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1answer
2k 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 ...
3
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1answer
2k 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 ...
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0answers
504 views

Interpret regression with Heteroskedasticity Corrected Standard Errors in R

In my data I have problems with heteroscedasticity as indicated by the Breusch-Pagan test and the NVC test that are both significant. Therefore, I would like to follow the method posted by Gavin ...
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1answer
28 views

In what way can a method provide better estimated than another method that already provides unbiased estimates?

I read in Rand Wilcox's (2010) book: "If homogeneity of variance cannot be assumed (with regard to regression), although the method of least squares still provides you with unbiased estimates of ...
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0answers
132 views

Robust Statistics - linear regression

Is it fair to say that robust estimation should be used when the iid assumption doesn't hold? My understanding is that the sample has to be random/independent/uncorrelated but it can follow non-...
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0answers
68 views

Regression model for testing the difference between the groups in presence of the covariate

I have a regression model like this lm(ContiniousVariable ~ ContiniousCovariate + BinaryCategoricalVariable) I repeat this model for each of the 20 ...
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0answers
329 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 ...
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0answers
123 views

Implementing analytic block-cluster-robust standard errors

How do we calculate block-cluster-robust SEs for the average-treatment effect? (Note, I do not want block bootstrap. I want the analytic estimate, calculated with block-population-weighted block-level ...
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1answer
1k views

HAC standard errors or robust standard errors?

When estimating an AR, ADL and VAR model, should I use robust standard errors or HAC errors? In an exercise, I used the robust standard error, and then check for autocorrelation in the residuls (...