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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3 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 ...
1
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0answers
14 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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0answers
5 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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0answers
22 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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0answers
24 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 ...
2
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0answers
18 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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0answers
75 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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0answers
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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223 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 ...
2
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0answers
28 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
48 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 ...
3
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1answer
76 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?
2
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1answer
306 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
223 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
776 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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0answers
184 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 ...
1
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1answer
84 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
233 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 ...
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0answers
34 views
Effect size robust regression (using robustbase in R)
I am using robustbase (lmrob) in R as I have outliers in my data.
Is there a way to calculate effect sizes? Standardized coeff or other effect sizes measures?
Thanks
4
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1answer
159 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} | \...
0
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0answers
82 views
cluster robust standard error, multiple levels
I am analyzing cross-sectional political candidate data by means of multiple linear regression.
Data structure: N=1850 candidates
individuals < political party-in-a district (party lists) (n=232) ...
3
votes
1answer
182 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 ...
2
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1answer
73 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 ...
2
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0answers
396 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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0answers
58 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 ...
2
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1answer
184 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-...
1
vote
1answer
744 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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0answers
68 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, ...
1
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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 ...
0
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2answers
311 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 ...
1
vote
1answer
235 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
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0answers
121 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 +...
1
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0answers
36 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 ...
5
votes
1answer
290 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} = ...
13
votes
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
964 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 ...
1
vote
3answers
432 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
308 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 ...
3
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0answers
154 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 ...
2
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2answers
210 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 ...
1
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0answers
279 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 ...
4
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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
votes
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 ...
0
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0answers
489 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 ...
1
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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-...
1
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0answers
66 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 ...
1
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0answers
317 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 ...
1
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0answers
118 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 ...
