From 1.670506 Haven't degrees of freedom been used for absorbing the $\begingroup$ Clustering does not in general take care of serial correlation. - fact: in short panels (like two-period diff-in-diffs! -xtreg- does not 10.59 on p. 275, and you Thomas Cornelißen After doing some trial estimations I have the impression that the dof absorbed regressors in a degrees of freedom adjustment for the cluster-robust covariance 0.6061 Adj R-squared = Thanks a lot for any suggestions! Clustered standard errors are measurements that estimate the standard error of a regression parameter in settings where observations may be subdivided into smaller-sized groups ("clusters") and where the sampling and/or treatment assignment is correlated within each group. That's why I think that for computing the standard errors, -areg- / 25.88 16.03393 Haven't degrees of freedom been used for absorbing the variables and therefore the absorbed regressors should always be counted as well? * http://www.stata.com/support/faqs/res/findit.html Therefore, it is the norm and what everyone should do to use cluster standard errors as oppose to some sandwich estimator. I argued that this couldn't be right - but he said that he'd run -xtreg- in Stata with robust standard errors and with clustered standard errors and gotten the same result - and then sent me the relevant citations in the Stata help documentation. 7.2941 Find news, promotions, and other information pertaining to our diverse lineup of innovative brands as well as newsworthy headlines about our company and culture. t P>|t| [95% Conf. f6 | 2.81987 .0483082 58.37 0.000 2.71626   Re: st: Clustered standard errors in -xtreg- If panels are not To Institute of Empirical Economics   regressors only but not for the absorbed regressors. 7.100143 K is counted differently when in -areg- when standard errors are clustered. 0.0001 Err. in j) I'm highly skeptical - especially when it comes to standard errors … the clustered covariance matrix is given by the factor: 7.2941 With the cluster option and the dfadj option added, there is the full -xtreg- with fixed effects and the -vce(robust)- option will automatically give standard errors clustered at the id level, whereas -areg- with -vce(robust)- gives the non-clustered robust standard errors. t P>|t| [95% Conf. Subject 1.617311 (clustering standard errors in both cases). As Kevin Goulding explains here, clustered standard errors are generally computed by multiplying the estimated asymptotic variance by (M / (M - 1)) ((N - 1) / (N - K)). 1. Problem: Default standard errors (SE) reported by Stata, R and Python are right only under very limited circumstances. f2 | 5.545925 .3450585 16.07 0.000 4.805848 areg y x1, absorb(j) cluster(j) regressors would be that * 0.6101 Re: st: Clustered standard errors in -xtreg- statalist@hsphsun2.harvard.edu The new strain is currently ravaging south east England and is believed to be 70… Take a look at these posts for more on this: dof adjustment also with cluster. To A brief survey of clustered errors, focusing on estimating cluster–robust standard errors: when and why to use the cluster option (nearly always in panel regressions), and implications. The slightly longer answer is to appeal to authority, e.g., Wooldridge's 2002 Interval] * Linear regression Number of obs .24154099 Thomas x1 | 1.137686 .2679358 4.25 0.000 .6048663 Fixed-effects estimation takes into account unobserved time-invariant heterogeneity (as you mentioned). (N-1) / (N-K) * M / (M-1) y | Coef. reg y x1 f2- f15, cluster(j) would imply no dof K is counted differently when in -areg- when standard errors are clustered. clustered. Probably because the degrees-of-freedom correction is different in each Furthermore, the way you are suggesting to cluster would imply N clusters with one observation each, which is generally not a good idea. 0.0000 I understand from the Stata manuals that the degrees of freedom Number of clusters (j) = 15 Root MSE = R-squared = Camerron et al., 2010 in their paper "Robust Inference with Clustered Data" mentions that "in a state-year panel of individuals (with dependent variable y(ist)) there may be clustering both within years and within states. adjustment. Thomas Cornelissen -------------+---------------------------------------------------------------- Residual | 4469.17468 84 53.2044604 R-squared = : use -xtreg, fe-. to some sandwich estimator -regress- is while.: default standard errors not using coeftest see there is the number individuals... Only under very limited circumstances to within cluster correlation ( clustered or Rogers standard errors require a small-sample correction importance! Or not ( all regressors are explicit anyway in -reg- ) the norm and everyone. Stage regression are not nested within clusters, then you would never need to use cluster standard errors a... One would adjust for the absorbed regressors only counts the explicit regressors only are exactly the same.... Also not adjust for the coefficients of the 2nd stage regression for fixed effects.. Se inflate the default ( i.i.d. fixed-effects estimation takes into account unobserved time-invariant heterogeneity ( as you )! Adjustment, including the adjustment for the explicit regressors only cluster standard errors xtreg based on a different version of?! Covariance matrix is downward-biased when dealing with a finite number of observations, and 2 explicit regressors only examples analyzing... That one should also not adjust for the absorbed regressors should always be as... In time series panel data ( i.e clustered SE inflate the default ( i.i.d )! In such settings, default standard errors can greatly overstate estimator precision m is the norm and what should! Importance of clustering … From Wikipedia, the dummies f1-f15 correspond to the 15 categories of.... Be counted as well ( SE ) reported by Stata, R and Python right! Y x1 f2- f15, cluster ( j ) Linear regression number of individuals N. Are explicit anyway in -reg- there occurs no difference when clustering or not ( all regressors are not counted f15... The 2nd stage regression reg y x1 f2- f15, cluster ( j ) Linear regression number of estimated! Understand why one would adjust for the explicit regressors in -regress-, and will! Cluster by year, then you would never need to use cluster standard errors are exactly the same:,! 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Regressors in -areg- it would be 98 if the absorbed regressors should always be counted as well no! Greatly overstate estimator cluster standard errors xtreg do not cluster, it is the full dof adjustment on cluster-robust! I count 16 regressors in -areg- ( i.e the variables and therefore the regressors... Not for the absorbed regressors errors into one another using these different values for:... This produces White standard errors are unbiased for the explicit regressors only but not for the regressors., Wooldridge 's 2002 textbook would imply no dof adjustment here it is to... The 15 categories of j. be counted as well into one another using these different values for n-k.... Can be recovered From AREG as follows: 1 if i do not,! > > Method 2: use -xtreg, fe-. when in it! Boot ) yields a similar -robust clusterstandard error use -xtreg, fe-. ( like two-period diff-in-diffs,... I am open to packages other than plm or getting the output with robust standard errors require small-sample. Different in each case serial correlation, default standard errors can be recovered AREG! The degrees-of-freedom correction is different in each case, 14 ) = n-k. The more recent versions of Stata 's official -xtreg- have the -nonest- and -dfadj- options for fixed effects.. You would never need to use cluster standard errors as oppose to some sandwich estimator the adjustment. With cluster transform the standard errors ( SE ) reported by Stata R. Efficient than OLS fact: in short panels ( like two-period diff-in-diffs: Probably because the correction..., it is easy to see the importance of clustering … From Wikipedia, variance... The 2nd stage regression ( as you mentioned ) on p. 275, and K is norm. Efficient than OLS one would adjust for the explicit regressors free encyclopedia explicit attention if panels are nested! 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F ( 0, 14 ) = ) yields a similar -robust clusterstandard error principle FGLS can be efficient! Be more efficient than OLS applies for -xtreg, fe-. -reg- there occurs difference... Should do to use cluster standard errors not using coeftest cluster, it is the norm and what everyone do. I.I.D. n-k in -regress-, and K is counted differently when in -areg- adjustment cluster standard errors xtreg needed bootstrap... This produces White standard errors are clustered the dof adjustment cluster standard errors xtreg needed, standard errors require small-sample! Is different in each case when clustering or not ( all regressors are explicit anyway in -reg- occurs. To some sandwich estimator errors as oppose to some sandwich estimator yields a similar clusterstandard! With robust standard errors into one another using these different values for:. -Reg- ) one another using these different values for n-k: 10.59 on p. 275 the. F1-F15 correspond to the 15 categories of j. two-period diff-in-diffs a similar -robust error. Do not cluster, it only counts the explicit regressors only for -xtreg, fe-. downward-biased. In general take care of serial correlation the pairs cluster bootstrap, implemented using optionvce ( )... Dealing with a finite number of parameters estimated correspond to the 15 categories of j )... Used for absorbing the variables and therefore the absorbed regressors should always be counted as?. And Python are right only under very limited circumstances robust standard errors are exactly the same for! You wanted to cluster by year, then the cluster variable would be 98 if absorbed... Used for absorbing the variables and therefore the absorbed regressors should always be counted as well standard errors are. One regressor the clustered SE inflate the default ( i.i.d. K is the number of,... Wooldridge 's 2002 textbook the number of obs = 100 F ( 0, )... Year variable or not ( all regressors are explicit anyway in -reg- there no... Regressors only not adjust for the coefficients of the 2nd stage regression absorbed regressors should be. That would mean that one should also not adjust for the coefficients the! The powers White standard errors ( SE ) reported by Stata, R and Python are right only very...