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.
115
questions
0
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0answers
23 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
votes
0answers
23 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 ...
1
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0answers
12 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
26 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
votes
2answers
18 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
36 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
19 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
35 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
11 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
17 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
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0answers
30 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
39 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 ...
0
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0answers
51 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
votes
0answers
25 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
votes
0answers
149 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 ...
0
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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 ...
0
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0answers
632 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
33 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
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1answer
81 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
116 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
499 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
0answers
26 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 ...
0
votes
1answer
668 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
1k 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
343 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
104 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
419 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
210 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
219 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
95 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
481 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
78 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
227 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
954 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 (...
0
votes
0answers
78 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
vote
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
votes
2answers
399 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
356 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
votes
0answers
133 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
vote
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 ...
6
votes
1answer
342 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
votes
1answer
2k 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?
...
0
votes
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 ...
1
vote
3answers
554 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 ...
0
votes
0answers
341 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
votes
0answers
190 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
votes
2answers
234 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
vote
0answers
321 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
votes
1answer
3k 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
3k 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 ...
