Clustered errors
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Clustered errors
Has anybody estimated clustered standard errors with Eviews? I know that it can be done with Stata, but my preferred option would be Eviews.
Thanks
Thanks

 Fe ddaethom, fe welon, fe amcangyfrifon
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Re: Clustered errors
EViews doesn't have this built in at the moment.
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 EViews Developer
 Posts: 2646
 Joined: Wed Oct 15, 2008 9:17 am
Re: Clustered errors
Not quite. Set it up as a panel with your cluster as the crosssection identifier. Then there are various options for grouped robust errors on the crosssection dimension.
Re: Clustered errors
Hi!
In fact, there seem to be several methods.
However, sofar I cannot see, which of the methods allows for
* nonidentivcally distributed disturbance terms and
* correlations of disturbance terms within a cluster.
These two features seem o be of particular relevance in
empircal corporate finance (and seem to be standard for
researchers using stata).
Any help is highly appreciated!
Best regards...msr
In fact, there seem to be several methods.
However, sofar I cannot see, which of the methods allows for
* nonidentivcally distributed disturbance terms and
* correlations of disturbance terms within a cluster.
These two features seem o be of particular relevance in
empircal corporate finance (and seem to be standard for
researchers using stata).
Any help is highly appreciated!
Best regards...msr

 EViews Developer
 Posts: 2646
 Joined: Wed Oct 15, 2008 9:17 am
Re: Clustered errors
If you define your panel with the cluster variable identifying the crosssection dimension, the "White crosssection" covariance option will estimate using what Arellano (Panel Data Econometrics, 2003, p. 18) terms the fixed T and large N robust standard error that is robust to heteroskedasticity and serial correlation (within cluster) of arbitrary form.
Last edited by EViews Glenn on Thu Jan 07, 2016 9:57 am, edited 2 times in total.
Re: Clustered errors
Thanks very much. I'll try to apply the recommended procedure.
Re: Clustered errors
Dear Glenn
Sorry for bothering you one more, but I still have problems with these options.
In the help file it says that
Using white period standard errors & covariance gives SEs that are
Using white crosssection standard errors & covariance gives SEs that are
Given your answer, it seems that the latter is what I am looking for. Now, using the latter gives me (sometimes) suprisingly small SEs (in a panel data set with large N and small T).
Again, what I am looking is a method that allows (in a panel data set with large N [firms] and small T [periods]) for
* crosssection heteroskedasticity, i.e. for a different residual variance for each cross section
* arbitrary correlation within a cluster [i.e. within a firm]
Any help is highly appreciated!
Best regards...msr
Sorry for bothering you one more, but I still have problems with these options.
In the help file it says that
Using white period standard errors & covariance gives SEs that are
…robust to arbitrary serial correlation and timevarying variances in the disturbances. [...] The White period robust coefficient variance estimator is designed to accommodate arbitrary serial correlation and timevarying variances in the disturbances.
Using white crosssection standard errors & covariance gives SEs that are
…crossequation (contemporaneous) correlation as well as different error variances in each crosssection.
Given your answer, it seems that the latter is what I am looking for. Now, using the latter gives me (sometimes) suprisingly small SEs (in a panel data set with large N and small T).
Again, what I am looking is a method that allows (in a panel data set with large N [firms] and small T [periods]) for
* crosssection heteroskedasticity, i.e. for a different residual variance for each cross section
* arbitrary correlation within a cluster [i.e. within a firm]
Any help is highly appreciated!
Best regards...msr

 EViews Developer
 Posts: 2646
 Joined: Wed Oct 15, 2008 9:17 am
Re: Clustered errors
Sorry about the confusion, it should be White period, which allows for arbitrary period correlation structures.
See Arellano (2003). Panel Data Econometrics, Section 2.3.1. p. 18. Requires asymptotics in N and fixed T.
See Arellano (2003). Panel Data Econometrics, Section 2.3.1. p. 18. Requires asymptotics in N and fixed T.
Re: Clustered errors
Dear Glenn
I have read the earlier posts, but I am still confused and I don't know whether to use White Priod or White CrossSection SE & covariance.
I do not do panel analysis, but  as you suggested in an earlier post  I have set up the data as a panel with the cluster variable [issuer] as the crosssection dimension.
I would like to have heteroskedasticityrobust standard errors clustered at the issuerlevel, in order to allow for correlation in standard errors that is specific to an issuer.
Thanks for your help and patience!
Julia
I have read the earlier posts, but I am still confused and I don't know whether to use White Priod or White CrossSection SE & covariance.
I do not do panel analysis, but  as you suggested in an earlier post  I have set up the data as a panel with the cluster variable [issuer] as the crosssection dimension.
I would like to have heteroskedasticityrobust standard errors clustered at the issuerlevel, in order to allow for correlation in standard errors that is specific to an issuer.
Thanks for your help and patience!
Julia

 EViews Developer
 Posts: 2646
 Joined: Wed Oct 15, 2008 9:17 am
Re: Clustered errors
If your workfile is structured as a panel with "issuer" as the crosssection identifier and you want to allow correlation for observations within issuer, but not across issuers, then the White  period will allow for betweenperiod correlations (i.e., clustered by issuer).
The unfortunate thing is that there are two different conventions for naming these things which is why it's all a bit confusing. Let me see if I can provide some background to help clarify things.
The original econometric literature on SUR type models was based on macro models with different equations for each crosssection (in the typical case, country) where it was believed that observations were contemporaneously correlated. Roughly speaking this error variance structure was given the name "crosssectionally correlated" to indicate that crosssections were correlated. Hence the name of crosssection SUR for models which allowed for contemporaneous correlation but no betweenperiod correlation. Note that in this convention, the "crosssection" nomenclature references the dimension that has correlation.
A more recent statistics literature would refer to this data structure as clustered "by period", since for each period the observations are correlated, but there is no correlation between periods. In this world, the "period" references the unit for which there is withincorrelation, not the units across which there is correlation.
When we introduced the two different forms of SUR and White covariances we tried to match the terminology of the original econometrics literature (for better or worse). Thus, "White  Period", may be thought of as a White estimator where we assume that there may be betweenperiod correlation, where there is no correlation across crosssections. These data would be termed clustered by crosssection in the second literature.
Now personally, I find the clustering nomenclature to be more natural, but that may not be the case for macro time series people. We may, in the future, switch the labeling over, but would do so in a way that preserved backward compatibility.
I hope that this clarifies things.
The unfortunate thing is that there are two different conventions for naming these things which is why it's all a bit confusing. Let me see if I can provide some background to help clarify things.
The original econometric literature on SUR type models was based on macro models with different equations for each crosssection (in the typical case, country) where it was believed that observations were contemporaneously correlated. Roughly speaking this error variance structure was given the name "crosssectionally correlated" to indicate that crosssections were correlated. Hence the name of crosssection SUR for models which allowed for contemporaneous correlation but no betweenperiod correlation. Note that in this convention, the "crosssection" nomenclature references the dimension that has correlation.
A more recent statistics literature would refer to this data structure as clustered "by period", since for each period the observations are correlated, but there is no correlation between periods. In this world, the "period" references the unit for which there is withincorrelation, not the units across which there is correlation.
When we introduced the two different forms of SUR and White covariances we tried to match the terminology of the original econometrics literature (for better or worse). Thus, "White  Period", may be thought of as a White estimator where we assume that there may be betweenperiod correlation, where there is no correlation across crosssections. These data would be termed clustered by crosssection in the second literature.
Now personally, I find the clustering nomenclature to be more natural, but that may not be the case for macro time series people. We may, in the future, switch the labeling over, but would do so in a way that preserved backward compatibility.
I hope that this clarifies things.
Re: Clustered errors
Thank you very much! This helped me a lot.
Regards,
Julia
Regards,
Julia

 Posts: 3
 Joined: Mon May 17, 2010 12:20 am
Re: Clustered errors
If you define your panel with the cluster variable identifying the crosssection dimension, the "White crosssection" covariance option will estimate using what Arellano (Panel Data Econometrics, 2003, p. 18) terms the fixed T and large N robust standard error that is robust to heteroskedasticity and serial correlation (within cluster) of arbitrary form.
what if the cluster variable is Country but the way my file is set up Firm is the main crosssection identifier and Country is just one of the many characteristics of the Firm eg the Firm is based in the USA. How can I manipulate the file to make Country the cluster variable so I can use the "White crosssection" covariance option?
Thank you!

 EViews Developer
 Posts: 2646
 Joined: Wed Oct 15, 2008 9:17 am
Re: Clustered errors
As I noted earlier, since we don't cluster arbitrarily, you'll have to restructure as an unbalanced panel on country for the purpose of computing country clustered errors.
Re: Clustered errors
Hi, I want to estimate a crosssectional regression of individual subject’s income on several individual parameters. However these data were obtained through several experimental sessions. My professor suggested that there could be intersession correlation between the outcomes, which I should account for. Can I organize my data, although it’s not a panel, as a panel and while estimating it as a panel regression correct for clustered errors? There are 8 subjects in each of the 5 sessions. Each subject participated only in one experimental session. I created a balanced panel where I set “session” as a crossidentifier and “subject” as a time variable. I am not familiar with paneldata regression and I’m very much concerned that setting subject as time variable is wrong? Thank you in advance!

 EViews Developer
 Posts: 2646
 Joined: Wed Oct 15, 2008 9:17 am
Re: Clustered errors
Yes to the basic idea, though with only 8 subjects, the asymptotics for the entire approach are a bit iffy.
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