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.
134
questions
0
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6 views
Adjusting standard errors for *both* heteroskedasticity and clusters in MATLAB?
My data is not exactly a panel, so I can't use the Panel Data Toolbox. It is over time (years) but not for the same industries. For example I have multiple rows with same industry in one year, and ...
1
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0answers
13 views
Why do heteroscedasticity-robust standard errors in logistic regression?
I am following a course on R. At the moment, we are working with logistic regression. The basic form we are taught is this one:
...
0
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0answers
10 views
Driscoll kraay estimator and R squared
I am doing a panel data regression with Driscoll-Kraay standard errors.
Do my R-squared and adjusted R-squared stay the same when using Driscoll-Kraay standard errors?
1
vote
1answer
41 views
Power analysis for cluster-robust regression
I am planning to run a regression for the model of the form:
$y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \beta_3x_1x_2 + \epsilon$
To determine the necessary sample size, I would usually use R's ...
1
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0answers
21 views
Why different regression result with real and excess return as the dependent variable?
I want to test how granted patent, R&D & their ratio predicts real & excess market return. I am doing 2 robust linear regressions:
\begin{align}
\text{Real Return} \left( t + 1 \right) =...
0
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0answers
16 views
Does the sigma-clipped variance/standard-deviation count as a robust estimator of scale?
Robust estimators of scale, such as the median absolute deviation (MAD) and so on, are less affected by outliers than something like the basic standard deviation/variance. Firstly, is there a specific ...
4
votes
1answer
54 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 ...
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0answers
13 views
SE of tobit goes to infinity if I include time trend variable (year variable)
I'm dealing with some data that is restricted to unmarried women with two or more children to find out if a tax policy affects their annual hours of work.
But, pretty much different from my ...
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0answers
16 views
Model Comparison Tests with Bootstrapped Models
I've found myself in a catch-22 of wanting to conduct model comparison tests (like the Likelihood Ratio Test) but having to accommodate non-normal data. I know the LR test is invalid with Robust ...
0
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0answers
45 views
My simple regression is homoscedastic - should I use robust standard errors or both?
I've been asked to estimate the following simple regression with the 'appropriate' standard errors at 5% sig level and report the results in a table:
...
0
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0answers
33 views
R implementation of robust variance estimator for nlme fit
For GLS/LMM models we can define robust variance estimator as
$$\operatorname{Var}_{R}\left(\hat{\beta}_{W L S}\right)=\left(X^{T} \hat{V}^{-1} X\right)^{-1}\left\{X^{T} \hat{V}^{-1} \hat{V}_{0} \...
1
vote
1answer
87 views
Address unequal variance between groups before applying contrasts for a linear model? (r)
My Goal: I have an ordinal factor variable (5 levels) to which I would like to apply contrasts to test for a linear trend. However, the factor groups have heterogeneity of variance.
What I've done: ...
0
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0answers
38 views
Trouble interpreting GARCH output in R
I am trying to write a model involving stock returns and for it I ran a fGARCH model.
The code used is:
...
1
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0answers
27 views
generating Heteroskedasticity- & autocorrelation-consistent (HAC) standard errors for mixed effects (LMER) models [closed]
Is there a package/code for generating robust Heteroskedasticity- and autocorrelation-consistent (HAC) standard errors for mixed-effects models (specifically lmer models) in R?
I am aware of vcovHA, ...
2
votes
0answers
17 views
Robust regression for autocorrelation and heteroskedasticity - coefficients do not change, only standard errors change?
When using Newey-West robust standard errors to deal with heteroskedasticity and autocorrelation:
http://support.sas.com/kb/40/098.html
is it correct to state that the coefficients are not different ...
1
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0answers
13 views
Pooled panel regression with group-wise clustering by time
How can I compute t-statistics for the coefficients of the pooled panel regression model below such that I account for group-wise clustering by time?
$$
Y_{it} = \alpha + \beta x_{it} + \epsilon_{it}
...
0
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0answers
18 views
Calculating robust standard errors while leaving out fixed effects in ordered logit?
I am estimating an ordered logit equation with R and trying to calculate robust standard errors. I have several thousand fixed effects in the model, (U.S. counties), and some of these coefficients are ...
1
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0answers
45 views
Why do robust linear models give smaller standard errors?
I've always read and been told that for heteroskedastic errors a normal OLS fit will generate standard errors that are too small, leading to a false degree of confidence in coefficient estimates. ...
0
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0answers
21 views
Why cannot the sandwich standard errors be used together with the Kenward-Roger denominator? [duplicate]
This question is about mixed models, and the use of both the Kenward-Roger correction (K-R) for small sample sizes and robust (sandwich) estimators for standard errors in the same model. Is it ...
0
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0answers
20 views
lost significance when using robust errors in panel data analists
I am doing my first econometric analysis and I am in doubt if I have done something wrong, as I get unexpected results.
I am using the WHO health data referenced in this assignment:http://people....
1
vote
1answer
26 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}...
0
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0answers
38 views
Can I neglect the evidence of autocorrelation?
I am estimating a model based on time series, which comes from theoretical background (economic theory), and the specification is quite common in empirical literature. However, I find that estimated ...
0
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0answers
81 views
Lag selection for Newey-West estimation in a panel data framework with Stata
I have a panel dataset with N=21 countries and T=8 two-year periods which are mechanically correlated (2010-2011, 2011-2012, 2012-2013 and so on...). Given this structure, I thought I should have ...
2
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0answers
45 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? ...
2
votes
0answers
37 views
Relationship between expected absolute deviation and standard deviation
The standard deviation of a random variable $X$, $$\sigma = \sqrt{Var[{X}]} = \sqrt{E[X^2 - E[X]^2]}$$ is a very commonly used measure of "normal" distance from the mean of $X$. A much less frequently ...
0
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2answers
35 views
The statistic t for linear regression with robust standard error
I need to calculate the statistic t (without any softwares or this sort of things) for a linear regression with the robust standard errors already computed. I know that in order to get the t statistic ...
1
vote
1answer
75 views
Constant changes in time series model
I am estimating a time series model using OLS. The LHS variable has a downtrend across the period. There is certainly autocorrelation on both the LHS and RHS.
the regression is:
$us10yr = \alpha +...
2
votes
0answers
24 views
Standard error clustering: Does it depend on the variable of interest?
Suppose I am interested in whether a students age affects their performance, and I run the following regression:
$performance(i) = \beta_1 age(i) + \beta_2 Female(i)+\beta_3 classsize(i)+\varepsilon(...
0
votes
1answer
47 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 ...
0
votes
0answers
40 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
vote
0answers
21 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 ...
0
votes
0answers
56 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
...
1
vote
1answer
108 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 ...
2
votes
0answers
26 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 ...
0
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0answers
1k 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
votes
0answers
54 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)^{-...
0
votes
1answer
201 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
votes
1answer
203 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
702 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 ...
0
votes
1answer
1k 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 ...
1
vote
2answers
2k 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 ...
1
vote
0answers
492 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
vote
1answer
129 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
3answers
623 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
votes
1answer
265 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} | \...
3
votes
1answer
270 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
votes
1answer
124 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
votes
0answers
564 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 ...
0
votes
0answers
88 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
votes
1answer
284 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-...
