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.
155
questions
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2answers
53 views
+50
Correcting (or bootstrapping) the standard errrors for a two stage glm
Cross posted on StackOverflow
I want to somehow correct the standard errors of my two stage residual inclusion, where in contrast to the 2SLS, the residuals are included in addition to the ...
0
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0answers
25 views
How do I interpret the results from xtprobit random effects model with robust standard errors in stata?
My probit model has panel data. The dependent variable is a binary outcome that survives = 1 and not survives = 0. I am estimating South Africa's export trade relationships that are import-product ...
0
votes
1answer
48 views
Newey-West (1987) t-stats
I have a time-series which is autocorrelated by construction. I have calculated the sample mean of this time-series, and would like to calculate the t-statistic corresponding to the hypothesis that ...
0
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0answers
9 views
Which standard errors should I use in the quaids command in stata?
I am estimating an Almost Ideal Demand System (AIDS) for different meat types using the stata command quaids to calculate price and income elasticities. I have household consumption data over several ...
0
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0answers
7 views
RLM Residuals in Statsmodel Python
I am trying to generate response type residual (y^-y)for RLM using statsmodel:
model=smf.rllm('y~x', data=x)
results=model.fit()
resid=results.resid
I have ...
1
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0answers
18 views
Heteroscedasticity-consistent standard error is not the same as OLS standard error
Problem
I perform the following regression
$$ y_i = a + b x_i +\epsilon, \quad \quad i = 1, ..., 21$$
I managed to estimate $\hat a = −0.59$ and $\hat b = 0.069$ with standard error $s.e.(\hat b) = 0....
1
vote
1answer
27 views
Are there formal measures for classifier or regression robustness?
Are there performance measures that produce a numerical value of the robustness of a classifier or regression.
By robustness I mean graceful degradation in performance to unexpected input (similar to ...
0
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0answers
7 views
Are Poisson regression with robust SEs and Negative Binomial regression NB1 acceptable when the outcome is the sum of dependent Poisson distributions?
The sum of two variables $X_1$ and $X_2$ with Poisson distribution that are not independent has a Hermite distribution. It seems to me, however, that such distribution is not very popular (I only ...
0
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2answers
45 views
Cox models in R: cluster, strata, robust, and frailty … modeling grouped/clustered survival data
Consider a situation where we have individual patient survival data from a series of clinical trials of patients treated in a similar way. We might have a dataset that looks like this, with n total ...
0
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0answers
11 views
Multi-level model with crossed random effects, robust standard erorrs yield different results
I'm having trouble setting up a multi-level model with crossed random effects (I just started
getting into the topic).
My data:
Observations (n = 122) are an appearance of a speaker (n = 81) at an ...
3
votes
0answers
60 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 ...
1
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0answers
28 views
Robust standard errors under overlapping observations: confusing simulation results from alternative methods
I have a time series of $h$-step-ahead forecasts $\hat{y}_{t+h|t}$ for $t=1,\dots,T-h$ with $h>1$. I also have the corresponding realized values of their targets $y_{t+h}$ and the corresponding ...
1
vote
0answers
18 views
Standard error for panel data models and heteroscedastic robust SE
There is something I do not understand on a deeper level on standard errors for panel data models. I know the reason for not using usual heteroscedastic-robust standard-errors is because of auto ...
2
votes
1answer
55 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:
...
1
vote
0answers
49 views
How to manually inflate standard errors to approximate clustered SEs
I'm reading a handout on clustering here
It's not clear to me how to compute $\rho_x$ or $\rho_\epsilon$. What is meant by within-cluster correlation of the regression, or within-cluster error ...
1
vote
0answers
58 views
How to decide between different robust standard errors?
Specifying my model I ran into some very mild heteroscedasticity problems. Given its superior small-sample properties (my dataset contains 79 observations) I used the HC3 specification of the White ...
5
votes
2answers
123 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 ...
3
votes
1answer
120 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 ...
0
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0answers
117 views
Wald test / seemingly unrelated regression on models with clustered standard errors in R
I am trying to conduct a Wald test (aka seemingly unrelated regression) on multivariate models with clustered standard errors in R. This is easy to do in Stata, but I cannot figure out how to do it in ...
0
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0answers
30 views
In a multilinear regression, does the usage of White standard errors always correct for heteroscedasticity?
I've calculated a multilinear regression. When testing the assumptions of linear regression, I've come to understand that my model violates the assumption of homoscedasticity (as shown with a Breusch-...
1
vote
1answer
326 views
Mixed effects negative binomial with robust standard errors (Huber-white) in R
I would like to fit a random effects model in R using the negative binomial distribution and reporting robust standard errors.
I was going to try using the sandwich package to compute the robust ...
0
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0answers
15 views
Scale factor for trimmed standard deviations?
For normally distributed data, what is the scale factor of a trimmed standard deviation?
For instance, let's say we have a normal distribution with $\sigma = 1$. If we remove the top and bottom $t<...
1
vote
1answer
103 views
Robust SE clustered GLM Gamma Log Link to match GEE Robust SE
How do I get the robust standard errors/sandwich variance estimators for GLM using a Gamma family with a log-link to match the robust standard errors from the GEE output?
...
0
votes
1answer
71 views
Test for weak instruments in Stata when using VCE robust [closed]
does anyone know how I can test for weak instruments (one instrument, just identified model) after 2SLS regression in Stata when using robust standard errors (VCE robust)?
0
votes
0answers
12 views
Adjusting p values when doing several comparisons between nested linear models
I have a linear model (in R) like this one (all variables are continous):
mod0 <- lm_robust(DV ~ IV1 + IV2 + IV3 + IV4, data = df)
(DV is stage of acquisition ...
0
votes
0answers
31 views
Sandwich estimators, robust standard errors
I'm using R for modeling linear regression for prediction. Residuals do not met regression assumptions of homoscedasticity, therefore using library(lmtest) and library("sandwich") I've ...
0
votes
0answers
97 views
How to use HAC errors in VAR model
I would like to use the Newey-West method for the standard errors in a VAR(p)-model (I use statsmodels.tsa.vector_ar). The VAR(p)-model assumes that the residuals ...
0
votes
0answers
65 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 ...
2
votes
0answers
84 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
votes
0answers
49 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
0answers
13 views
In regression modeling, are there any caveats to always using robust standard errors?
Aside from efficiency issues, is there anything else to this?
1
vote
1answer
70 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
29 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
votes
0answers
59 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
169 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 ...
0
votes
0answers
72 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:
...
3
votes
1answer
205 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: ...
1
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0answers
46 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, ...
3
votes
0answers
50 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
vote
0answers
20 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?
$$
Y_{it} = \alpha + \beta x_{it} + \epsilon_{it}
...
1
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0answers
158 views
Why do robust linear models give smaller standard errors?
I've always read and been told that for heteroskedastic errors a normal OLS fit will generate standard errors that are too small, leading to a false degree of confidence in coefficient estimates. ...
2
votes
1answer
33 views
Variance Estimation for Least Squares with Probability Weights
I'm running a simulation study and finding that the nominal SEs of the estimated coefficients when using weights in lm in R are an underestimate of the simulation SE. I have confirmed that $\hat{\beta}...
0
votes
0answers
43 views
Can I neglect the evidence of autocorrelation?
I am estimating a model based on time series, which comes from theoretical background (economic theory), and the specification is quite common in empirical literature. However, I find that estimated ...
2
votes
0answers
120 views
Survival analysis: Frailty vs Sandwich variance estimators
Question
Can someone answer (in as non-technical terms as possible) whether or not frailty models and robust sandwich variance estimators are trying to solve the same problem in different contexts? ...
2
votes
0answers
78 views
Relationship between expected absolute deviation and standard deviation
The standard deviation of a random variable $X$, $$\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 ...
1
vote
2answers
188 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
204 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
32 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
82 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 ...
1
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0answers
28 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 ...
