Questions tagged [clustered-standard-errors]

Clustered standard errors represent the version of the general sandwich variance estimator that correct for (potential) grouping of the observations, e.g., repeated measurements clustered within an individual, or individuals clustered within a hierarchy level (geographical region, educational institution, etc.).

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12
votes
1answer
9k views

OLS with clustered standard errors vs. multilevel modeling when the main interest is at the individual level [duplicate]

Possible Duplicate: Under what conditions should one use multilevel/hierarchical analysis? I have been reading various papers dealing with multilevel analysis, and to be honest, I am still ...
35
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4answers
47k views

Standard error clustering in R (either manually or in plm)

I am trying to understand standard error "clustering" and how to execute in R (it is trivial in Stata). In R I have been unsuccessful using either plm or writing my ...
7
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3answers
6k views

panel data - within-group estimate - individual fixed effects retrieved

I am analyzing panel data. First, I have to decide whether to use a random or fixed effect estimator. The Hausman test suggests to use the fixed effect estimator (also named within group estimator). ...
6
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2answers
1k views

Clustered standard errors - Why are SE smaller or bigger than OLS depending on cluster level?

I am analyzing some data using an OLS model. Data represent managers working for US cities. Within each city, we surveyed more than one manager (max 5). Multiple cities per state were surveyed. I'd ...
6
votes
1answer
521 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} = ...
4
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2answers
3k views

Clustering high-dimensional sparse binary data

I am trying to cluster Facebook users based on their likes. I have two problems: First, since there is no dislike in Facebook all I have is having likes (1) for some items but for the rest of the ...
3
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1answer
8k views

Minimum cluster size requirements? Minimum sample size requirements for clustered standard errors?

I have a sample with little over 100 observations and 50 clusters, one quarter of which have only one observation. Is it correct to calculate clustered standard errors in a linear regression that uses ...
25
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3answers
44k views

When to use fixed effects vs using cluster SEs?

Suppose you have a single cross-section of data where individuals are located within groups (e.g. students within schools) and you wish to estimate a model of the form ...
8
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1answer
4k views

Clustered standard errors and multi-level models

Stata allows estimating clustered standard errors in models with fixed effects but not in models random effects? Why is this? By clustered standard errors, I mean clustering as done by stata's ...
6
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1answer
6k views

cluster-robust standard errors are smaller than unclustered ones in fgls with cluster fixed effects

I'm currently working on some experimental data. The experimental design consists of two treatments. In each treatment, 20 subjects are randomly matched in pairs and participate to a simple game. The ...
4
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2answers
2k views

Implausibly small standard error

I have data of operation success of many doctors. I estimated a regression using Stata with fix effects on the individual doctors. I first ran the regression using robust option. The resulted t value ...
3
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1answer
1k views

Clustered (grouped) standard errors MLE in R

I'm doing the following maximum likelihood estimation using mle2 function from bbmle package: ...
3
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1answer
170 views

How to cluster standard errors when fitting a model using maximum likelihood estimation in R?

I am estimating several parameters using the mle() function with the L-BFGS-B method in R to data from an experiment in which participants had to make multiple choices. My goal is to obtain population ...
3
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1answer
4k views

Clustered standard errors and pooled OLS

Can someone please explain why clustered standard errors ensure that the error estimates of Pooled OLS results are appropriate?
3
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1answer
5k views

How to decide on the clustering of standard errors? (region- vs. country-level)

I'm estimating a first-difference panel data model with data on the regional level (~125 regions). All regions are part of a country (~12 countries). It seems intuitive to cluster the standard errors,...
2
votes
3answers
7k views

What level should I cluster at in my regression?

So I am running a pooled OLS. My dependent variable is house prices, and my main explanatory variables are job creation and distance to job creation, plus a raft of control variables. The housing ...
1
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1answer
5k views

Clustered standard errors and robust standard errors

I was wondering if, when running a regression on panel data, clustered standard errors are already correcting for heteroskedasticity. Actually, I have run such a regression and detected ...
1
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1answer
1k views

Procedure for the cluster-robust Hausman test

The Hausman test cannot be run on robust std. errors we have separately make the FE and RE standard errors robust to serial correlation and heteroskedasticity by clustered standard errors. So, is ...
1
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0answers
146 views

hierarchical bootstrapping and calculation of variance (in a Random-Effects ROC Analysis) in R

I would like to calculate the variance of the AUC of readers (for each reader and averaged results) giving a score(1-5) to ...