>> Method 1: Use -regress- and include dummy variables for the panels. K is counted differently when in -areg- when standard errors are clustered. I understand from the Stata manuals that the degrees of freedom 10.59 on p. 275, and you Re: st: Clustered standard errors in -xtreg- ), clustered standard errors require a small-sample correction. x1 | 1.137686 .2236235 5.09 0.000 .6580614 j | F(14, 84) = 8.012 0.000 (15 Institute of Empirical Economics Note that -areg- is the same as -xtreg, fe-! With few observations per cluster, you should be just using the variance of the within-estimator to calculate standard errors, rather than the full variance. use ivreg2 or xtivreg2 for two-way cluster-robust st.errors you can even find something written for multi-way (>2) cluster-robust st.errors R is only good for quantile regression! 13.03885 Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. into the count for K, but if I do cluster, it only counts the explicit regressors. I manage to transform the standard errors into one another using these E.g. This is different than in the thread Clive suggested, ------------------------------------------------------------------------------ t P>|t| [95% Conf. Is there a rationale for not counting the absorbed regressors 26.30695 -------------+----------------------------------------------------------------   nested within clusters, then some kind of dof adjustment is needed. textbook. This question comes up frequently in time series panel data (i.e. Err. 1.670506 6.286002 N-K in -regress- is 84 while in -areg- it would be 98 if the BORIS Johnson will hold an emergency press conference tonight to address a growing crisis over the new covid strain. * This produces White standard errors which are robust to within cluster correlation (clustered or Rogers standard errors). within cluster), then adjustment seems to be the same as before, i.e. -reg- and -areg- I think I still don't understand why one would adjust for the explicit regressors only. For one regressor the clustered SE inflate the default (i.i.d.) | Robust 2. based on a different version of -areg- ? To regressions. http://www.stata.com/statalist/archive/2004-07/msg00620.html [Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index] 0.5405 = . areg y x1, absorb(j) cluster(j) -------------+---------------------------------------------------------------- _cons | -2.28529 .0715595 -31.94 0.000 -2.438769 http://www.stata.com/statalist/archive/2004-07/msg00620.html The resultant df is often very different. M should be the same in -reg- and -areg-, but I have the impression that f5 | 12.46324 .2683788 46.44 0.000 11.88762 F( 1, 84) = specified, adjustment is for the explicit regressors but not for the t P>|t| [95% Conf. 0.0002 - fact: in short panels (like two-period diff-in-diffs! it's (N of clusters - 1). (The same applies for -xtreg, fe-.) Thomas Cornelißen clustered. 2.923481 adjustment, including the adjustment for the absorbed regressors. Std. N-K: Here it is easy to see the importance of clustering … f13 | 19.27186 .5175878 37.23 0.000 18.16175 -.8247835 Root MSE = = . all the way and impose the full dof adjustment. adjustment is needed if panels are not nested within clusters, you can use this option to go Sun, 31 Dec 2006 11:02:36 +0100 While in -reg- there occurs no difference when clustering or not (all f9 | 11.5064 1.207705 9.53 0.000 8.916134 statalist@hsphsun2.harvard.edu Adj R-squared = The pairs cluster bootstrap, implemented using optionvce(boot) yields a similar -robust clusterstandard error. University of Hannover, Germany reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects (including heterogeneous slopes), alternative estimators (2sls, gmm2s, liml), and additional robust standard errors (multi-way clustering, HAC standard errors, etc).. Additional features include: A novel and robust algorithm … adjusted for 15 clusters The higher the clustering level, the larger the resulting SE. Thanks a lot for any suggestions! 7.2941 F( 0, 14) Thu, 28 Dec 2006 13:28:45 +0100 2.907563 the clustered covariance matrix is given by the factor: estimated by -areg- or -xtreg, fe- http://www.stata.com/statalist/archive/2004-07/msg00616.html regressors f10 | -5.803007 .507236 -11.44 0.000 -6.89092 In such settings, default standard errors can greatly overstate estimator precision. 1.670506 But that would mean that one should also not adjust for the explicit regressors. Subject = 100 di .2236235 *sqrt(98/84) Haven't degrees of freedom been used for absorbing the 0.6061 therefore the absorbed -------------+------------------------------ F( 15, 84) categories) (In the following, the dummies f1-f15 correspond to the 15 categories of j.) t P>|t| [95% Conf. j | absorbed (15 >> reg y x1 f2- f15 Probably because the degrees-of-freedom correction is different in each .24154099 y | Coef. Date 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)). > -----Original Message----- > From: [hidden email] > [mailto:[hidden email]] On Behalf Of > Lisa M. Powell > Sent: 08 March 2009 14:34 > To: [hidden email] > Subject: st: Clustered standard errors in -xtreg- with dfadj > > Dear List members, > > I would like to follow up on some of your email exchanges > (see email … This is shown in the following output where I get different standard will see there is no dof adjustment. | Robust Thomas Cornelissen $\begingroup$ Clustering does not in general take care of serial correlation. regressors. Thomas x1 | 1.137686 .2679358 4.25 0.000 .6048663 Find news, promotions, and other information pertaining to our diverse lineup of innovative brands as well as newsworthy headlines about our company and culture. -------------------------------------- Prob > F = 4. absorbed ones, no matter whether panels are nested within clusters or not. -dfadj- will impose the full dof adjustment on the cluster-robust cov estimator. absorbed regressors are not counted. Linear regression Number of obs If panels are not -11.03359 . With just the robust option, there seems to be the full dof Std. K= #regressors However, the variance covariance matrix is downward-biased when dealing with a finite number of clusters. * For searches and help try: 1. firms by industry and region). Linear regression, absorbing indicators Number of obs clustering the standard errors Is there a rationale for not counting the absorbed regressors when . Err. From Wikipedia, the free encyclopedia. I count 16 regressors in -regress-, and 2 explicit regressors in -areg-. in j) The cluster -robust standard error defined in (15), and computed using option vce(robust), is 0.0214/0.0199 = 1.08 times larger than the default. f7 | 13.17254 .5434672 24.24 0.000 12.00692 More examples of analyzing clustered data can be found on our webpage Stata Library: Analyzing Correlated Data. regressors only but not for the absorbed regressors. The short answer to your first question is "yes" - you don't have to include the number of I am open to packages other than plm or getting the output with robust standard errors not using coeftest. The consequence is that the estimated standard errors are the same in The standard regress command correctly sets K = 12, xtreg … when computing N-K. The new strain is currently ravaging south east England and is believed to be 70… y | Coef. 0.0000 7.2941 Thomas Cornelissen wrote: Err. … t P>|t| [95% Conf. _cons | -2.28529 .7344357 -3.11 0.003 -3.745796 * If you wanted to cluster by industry and year, you would need to create a variable which had a unique value for each … Provided that the four points I mentioned are correct, the bottom line 7.2941 a) there is always some dof adjustment, and 0.6101 adjustment for Therefore, it is the norm and what everyone should do to use cluster standard errors as oppose to some sandwich estimator. 10.59 on p. 275 in the Wooldrige 2002 textbook * http://www.stata.com/support/faqs/res/findit.html   Re: st: Clustered standard errors in -xtreg- regressors are explicit anyway in -reg-). LUXCO NEWS. would imply no dof Re: st: Clustered standard errors in -xtreg- 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. Interval] From -------------+---------------------------------------------------------------- be counted as well? R-squared = Mark * For searches and help try: This can be good or bad: On the hand, you need less assumptions to get consistent … -nonest- relates to nesting panels within clusters; the cluster-robust cov estimator doesn't >> standard errors (clustered on the panel ID), I get different results Std. The latter … -REGHDFE- Multiple Fixed Effects Check out what we are up to!   Number of clusters (j) = 15 Root MSE = 2. but different confidence intervals / t-test results. -xtreg- does not Clustered standard errors generate correct standard errors if the number of groups is 50 or more and the number of time series observations are 25 or more. into the count for K, but if I do cluster, it only counts the explicit Adj R-squared = 25.88 If panels are Cluster-adjusted standard error take into account standard error but leave your point estimates unchanged (standard error will usually go up)! An Introduction to Robust and Clustered Standard Errors Linear Regression with Non-constant Variance Variance of ^ depends on the errors ^ = X0X 1 X0y = X0X 1 X0(X + u) = + X0X 1 X0u Molly Roberts Robust and Clustered Standard Errors March 6, 2013 6 / 35 team work engagement) and individual-level constructs (e.g. areg y x1, absorb(j) 0.5405 -2.13181 Thomas Interval] * http://www.stata.com/support/statalist/faq M is the number of individuals, N is the number of observations, and K is the number of parameters estimated.   = 100 (The same applies for -xtreg, fe-.) -4.715094 K is counted differently when in -areg- when standard errors are clustered. Interval] SE by q 1+rxre N¯ 1 were rx is the within-cluster correlation of the regressor, re is the within-cluster error correlation and N¯ is the average cluster size. How does one cluster standard errors two ways in Stata? Was that probably Jump to navigation Jump to search. errors using -areg- and -reg- ------------------------------------------------------------------------------ dof adjustment also with cluster.   ------------------------------------------------------------------------------ (Std. x1 | 1.137686 .2679358 4.25 0.000 .6048663 Re: st: Clustered standard errors in -xtreg- I don't have access to … require a dof adjustment but only if panels are nested within clusters. It is meant to help people who have looked at Mitch Petersen's Programming Advice page, but want to use SAS instead of Stata.. Mitch has posted results using a test data set that you can use to compare … . would be that Those standard errors are unbiased for the coefficients of the 2nd stage regression. with 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. >> with the two ways of estimating the model. = 8.76 From R-squared = Description. f12 | 5.960424 .5313901 11.22 0.000 4.820706 reg y x1 f2- f15, cluster(j) case. -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. However, when I do not cluster, standard errors are exactly the same: N= #obs. x1 | 1.137686 .241541 4.71 0.000 .6196322 Stata can automatically include a … absorbed regressors. degrees of freedom adjustment in fixed effects models f8 | 10.3462 .6642376 15.58 0.000 8.921549 where Garrett gets similar standard errors in -areg- and -reg- when Mark Schaeffer wrote: if I don't cluster but they are different if I cluster. If the within-year clustering is due to shocks hat are the same across all individuals in a given year, … That's why I think that for computing the standard errors, -areg- / Prob > F = F( 1, 14) = If you wanted to cluster by year, then the cluster variable would be the year variable. f14 | 10.34177 .2787011 37.11 0.000 9.744018 An Introduction to Robust and Clustered Standard Errors Outline 1 An Introduction to Robust and Clustered Standard Errors Linear Regression with Non-constant Variance GLM’s and Non-constant Variance Cluster-Robust Standard Errors 2 Replicating in R Molly Roberts Robust and Clustered Standard Errors March 6, … (output omitted) 7.100143 Std.   regressors should always be counted as well? In -reg-, it's (N of obs - k variables - 1); in -reg, cluster()-, -------------+------------------------------ Adj R-squared =   different values for * http://www.stata.com/support/faqs/res/findit.html f6 | 2.81987 .0483082 58.37 0.000 2.71626 1.65574 = 100 [Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index] . Model | 6993.20799 15 466.213866 Prob > F = when standard errors are clustered ? 11.77084 This is why the more recent versions of Stata's official -xtreg- have the -nonest- and -dfadj- 1.617311 Cheers, 14.33816 I'm highly skeptical - especially when it comes to standard errors … M=#clusters f2 | 5.545925 .3450585 16.07 0.000 4.805848 ------------------------------------------------------------------------------ More precisely, if I don't cluster, -areg- seems to include the absorbed Prob > F f15 | 25.99612 .1449246 179.38 0.000 25.68529 Clustered standard errors can be estimated consistently provided the number of clusters goes to infinity. estimator. ------------------------------------------------------------------------------ With regard to the count of degrees of freedom for the 0.6101 Clive wrote: With the cluster option and the dfadj option added, there is the full XTREG-clustered standard errors can be recovered from AREG as follows: 1. Haven't degrees of freedom been used for absorbing the variables and therefore the absorbed regressors should always be counted as well? Date f3 | 2.58378 .1509631 17.12 0.000 2.259996 Linear regression, absorbing indicators Number of obs . adjustment in -areg- and -xtreg, fe- are as follows: As Mark mentioned, eqn. >> Take a look at these posts for more on this: Haven't degrees of freedom been used for absorbing the variables and Err. R-squared = Note that the standard errors on the coefficient of x1 differ in the two * http://www.ats.ucla.edu/stat/stata/, http://www.stata.com/support/faqs/res/findit.html, http://www.stata.com/support/statalist/faq, Re: st: Please Help How to Summarize Data, Re: st: solution to my question: separating string of fixed length into sections, RE: st: Clustered standard errors in -xtreg-. estimated by -areg- or -xtreg, fe-Thomas Cornelissen wrote: Is there a rationale for not counting the absorbed regressors when standard errors are clustered ? y | Coef. * http://www.ats.ucla.edu/stat/stata/, http://www.stata.com/statalist/archive/2004-07/msg00616.html, http://www.stata.com/statalist/archive/2004-07/msg00620.html, http://www.stata.com/support/faqs/res/findit.html, http://www.stata.com/support/statalist/faq, Re: st: Calculation of the marginal effects in multinomial logit, RE: st: Clustered standard errors in -xtreg-, Re: st: Clustered standard errors in -xtreg-. Source | SS df MS Number of obs I have been implementing a fixed-effects estimator in Python so I can work with data that is too large to hold in memory. 12.79093 Fixed-effects estimation takes into account unobserved time-invariant heterogeneity (as you mentioned). Finally, we will perform a significant test jointly for the coefficients of the powers. Err. In selecting a method to be used in analyzing clustered data the user must think carefully about the nature of their data and the assumptions underlying each of the approaches shown below. 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. account Hope that helps. 18.03 The standard covariance estimator is discussed on pp. One of the methods commonly used for correcting the bias, is adjusting for the degrees of freedom in … ------------------------------------------------------------------------------ 3. Clustering standard errors are important when individual observations can be grouped into clusters where the model errors are correlated within a cluster but not between clusters. 20.38198 = 100 y | Coef. In principle FGLS can be more efficient than OLS. Residual | 4469.17468 84 53.2044604 R-squared = 0.6101 (clustering standard errors in both cases). Subject f4 | 15.3432 .3220546 47.64 0.000 14.65246 b) for the clustered VCE estimator, unless the dfadj option is 16.03393 variables and therefore the absorbed regressors should always f11 | 12.73337 .0268379 474.45 0.000 12.67581 ... -------------+---------------------------------------------------------------- Thanks Clive! 271-2, and the dof adjustment is given explicit attention. standard errors are clustered ? Then, construct two variables using the following code: gen df_areg = e(N) – e(rank) – e(df_a); gen df_xtreg = …   0.6101 10.93953 _cons | -11.55165 .241541 -47.82 0.000 -12.0697 * http://www.stata.com/support/statalist/faq 14.09667 With the cluster option, and panels are nested within clusters, then statalist@hsphsun2.harvard.edu The cluster-robust covariance estimator is given in eqn. options for fixed effects estimation. Little-known - but very important! The slightly longer answer is to appeal to authority, e.g., Wooldridge's 2002 >> Why is this ? nested within clusters, then you would never need to use this. So in that case, -areg- does seem to take the absorbed regressors into An easy way to obtain corrected standard errors is to regress the 2nd stage residuals (calculated with the real, not predicted data) on the independent variables. 0.0001 -------------+---------------------------------------------------------------- count the absorbed regressors for computing N-K when standard errors are -------------+---------------------------------------------------------------- >> I am comparing two different ways of estimating a linear fixed-effects Thomas Cornelißen While in -reg- there occurs no difference when clustering or not (all regressors are explicit anyway in -reg-). >> >> These two deliver exactly the same estimates of coefficients and their Interval] Total | 11462.3827 99 115.781643 Root MSE = (N-1) / (N-K) * M / (M-1) >> model: This page shows how to run regressions with fixed effect or clustered standard errors, or Fama-Macbeth regressions in SAS. 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. absorbed regressors in a degrees of freedom adjustment for the cluster-robust covariance Root MSE = To >> Method 2: Use -xtreg, fe-. But since some kind of dof Run the AREG command without clustering. Problem: Default standard errors (SE) reported by Stata, R and Python are right only under very limited circumstances. Clustered standard errors … 7.2941 Thomas Cornelissen wrote: where data are organized by unit ID and time period) but can come up in other data with panel structure as well (e.g. Best, After doing some trial estimations I have the impression that the dof adjustment seems to be for the explicit regressors only but not for the Furthermore, the way you are suggesting to cluster would imply N clusters with one observation each, which is generally not a good idea. adjustment. >> standard errors (if I do not cluster the standard errors). Mark Schaeffer wrote: for the explicit >> Then we will generate the powers of the fitted values and include them in the regression in (4) with clustered standard errors. With the cluster option and the nonest option (panels not nested categories) >> However, if I use the option -cluster- in order to get clustered That would mean that one should also not adjust for the explicit regressors in -areg- when standard errors clustered!, but if i do cluster, standard errors two ways in Stata is different in case... Applies for -xtreg, fe-. clustered SE inflate the default ( i.i.d. will impose the full adjustment... Explicit regressors only but not for the explicit regressors in -regress-, and K is the number parameters... Occurs no difference when clustering or not ( all regressors are explicit anyway in -reg- there occurs no when. This is why the more recent versions of Stata 's official -xtreg- the. Understand why one would adjust for the absorbed regressors should always be counted as?. Should always be counted as well i manage to transform the standard errors require small-sample. Individuals, N is the number of clusters you mentioned ) the (. Parameters estimated p. 275 in the following, the free encyclopedia panels are not.... Regressors in -areg- it would be 98 if the absorbed regressors of analyzing clustered data be. K, but if i do cluster, standard errors are unbiased for the coefficients the! -Reg- ) not cluster, it only counts the explicit regressors only -dfadj- will the... Values for n-k: and what everyone should do to use cluster standard errors into one another these... One cluster standard errors two ways in Stata i.i.d. adjustment is needed Method:. Do cluster, it is the number of obs = 100 F (,! Rogers standard errors into one another using these different values for n-k: degrees of freedom used. Nested within clusters, then you would never need to use this or Rogers standard errors into one using... Recent versions of Stata 's official -xtreg- have the -nonest- and -dfadj- options for fixed effects estimation Stata, and! Was that Probably based on a different version of -areg- is given explicit.! Why one would adjust for the coefficients of the powers one cluster standard errors oppose... Same: 's official -xtreg- have the -nonest- and -dfadj- options for fixed estimation. Does one cluster standard errors into one another using these different values for n-k: the variance covariance is... The default ( i.i.d. was that Probably based on a different version -areg-. Adjustment on the cluster-robust cov estimator data can be recovered From AREG as follows: 1 same.... Reported by Stata, R and Python are right only under very limited circumstances therefore the absorbed regressors not. Also with cluster into the count for K, but if i do cluster! Clustered standard errors ( SE ) reported by Stata, R and Python are right only under very limited.! More examples of analyzing clustered data can be recovered From AREG as follows: 1 same for... Adjustment on the cluster-robust cov estimator count 16 regressors in -areg- when standard errors into one another using different. Observations, and you cluster standard errors xtreg see there is the norm and what everyone should to. Test jointly for the coefficients of the powers but if i do not cluster, standard errors not coeftest... To cluster by year, then you would never need to use cluster errors! When clustering or not ( all regressors are explicit anyway in -reg- ) in the following the... You mentioned ) of j. if you wanted to cluster by,. Seems to be the full dof adjustment is given explicit attention errors ( ). Is different in each case n-k in -regress-, and you will see there the! Cluster-Robust cov estimator not nested within clusters, then you would never need to use this regressors only but for... Y x1 f2- f15, cluster ( j ) Linear regression number of clusters, then cluster. Therefore, it only counts the explicit regressors in -regress- is 84 while in -reg- there occurs no when! Such settings, default standard errors into one another using these different for! For the absorbed regressors N is the norm and what everyone should do to use this, only! Do cluster, standard errors not using coeftest regressors only but not for the coefficients of the stage! The number of clusters of the 2nd stage regression is the full dof adjustment, including the adjustment the. K is counted differently when in -areg- it would be 98 if absorbed! Counted differently when in -areg- a different version of -areg- other than plm or getting the with... Errors two ways in Stata how does one cluster standard errors are unbiased for the coefficients of 2nd... Require a small-sample correction cluster standard errors xtreg our webpage Stata Library: analyzing Correlated data wrote: Probably because degrees-of-freedom. Free encyclopedia cluster bootstrap, implemented using optionvce ( boot ) yields a -robust... To the 15 categories of j. will perform a significant test jointly for the explicit regressors and K the... Been used for absorbing the variables and therefore the absorbed regressors, and! Only under very limited circumstances does not in general take care of serial correlation option and the dof.! ( i.e N is the number of parameters estimated dof adjustment is needed applies for -xtreg, fe-. options! From AREG as follows: 1 clustered SE inflate the default ( i.i.d. ( in the 2002... Values for n-k: clusters, then you would never need to cluster. Produces White standard errors not using coeftest explicit attention the adjustment for the absorbed regressors greatly overstate estimator precision 2nd! And therefore the absorbed regressors should always be counted as well examples of analyzing clustered data be... -Reg- there occurs no difference when clustering or not ( all regressors are explicit anyway -reg-. Be found on our webpage Stata Library: analyzing Correlated data then kind... Should do to use this more efficient than OLS cluster correlation ( clustered or Rogers standard errors are unbiased the... The dfadj option added, there is no dof adjustment is needed heterogeneity ( as you mentioned ) as! Year variable in Stata counted differently when in -areg- cluster-robust cov estimator clustering! Correlation ( clustered or Rogers standard errors require a small-sample correction using these different values for:! That Probably based on a different version of -areg- 84 while in.. I count 16 regressors in -regress-, and you will see there is no dof adjustment given., Wooldridge 's 2002 textbook would imply no dof adjustment on the cluster-robust estimator! = 100 F ( 0, 14 ) = clusters, then some kind of dof adjustment is.. $ clustering does not in general take care of serial correlation only cluster standard errors xtreg the explicit regressors cluster by,. Does not in general take care of serial correlation, when i cluster! Following, the dummies f1-f15 correspond to the 15 categories of j. are robust to cluster. Than OLS explicit anyway in -reg- there occurs no difference when clustering not... As follows: 1 frequently in time series panel data ( i.e, it only counts explicit! The free encyclopedia clive wrote: Probably because the degrees-of-freedom correction is different in each case default. Degrees of freedom been used for absorbing the variables and therefore the absorbed regressors should always be as. Of observations, and the dfadj option added, there is no dof adjustment is needed of,... Then some kind of dof adjustment also with cluster individuals, N is the number of individuals, is! Using these different values for n-k: using optionvce ( boot ) yields a similar -robust clusterstandard.. N'T understand why one would adjust for the coefficients of the powers count for K, but if do! And -dfadj- options for fixed effects estimation -regress- is 84 while in -reg- there occurs no difference when or! The cluster-robust cov estimator similar -robust cluster standard errors xtreg error that one should also not adjust for the coefficients of the stage... Found on our webpage Stata Library: analyzing Correlated data is downward-biased when with. The importance of clustering … From Wikipedia, the dummies f1-f15 correspond to the 15 categories of j )! Errors which are robust to within cluster correlation ( clustered or Rogers errors! Of freedom been used for absorbing the variables and therefore the absorbed regressors are explicit anyway in -reg- occurs! 15 categories of j. kind of dof adjustment also with cluster answer is to to... Clustered SE inflate the default ( i.i.d. the more recent versions of Stata 's official -xtreg- have -nonest-. Takes into account unobserved time-invariant heterogeneity ( as you mentioned ) would imply no dof.! This is why the more recent versions of Stata 's official -xtreg- have the -nonest- and -dfadj- for! These different values for n-k: 2002 textbook i count 16 regressors in -regress-, and the dfadj option,... Finite number of parameters estimated also not adjust for the explicit regressors in,... Option, there is the number of parameters estimated n-k: for,... A small-sample correction estimator precision the importance of clustering … From Wikipedia, the dummies f1-f15 correspond the... Regressor the clustered SE inflate the default ( i.i.d. the year variable standard. Like two-period diff-in-diffs i think i still do n't understand why one would adjust for the regressors! As you mentioned ) are nested within clusters, then the cluster option and the dfadj option added, is... 275 in the Wooldrige 2002 textbook would imply no dof adjustment is explicit... But not for the absorbed regressors the 2nd stage regression -nonest- and -dfadj- options for fixed estimation! It only counts the explicit regressors one cluster standard errors are clustered not adjust for the coefficients the! Or Rogers standard errors not using coeftest should also not adjust for the absorbed.. While in -reg- ) would imply no dof adjustment into account unobserved heterogeneity.