It looks like you want to run a log(y) regression and then compute exp(xb). Without any adjustment, we would assume that the degrees-of-freedom used by the fixed effects is equal to the count of all the fixed effects (e.g. In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Note that fast will be disabled when adding variables to the dataset (i.e. Suppose I have an employer-employee linked panel dataset that looks something like this: Year Worker_ID Firm_ID X1 X2 X3 Wage, 1992 1 3 2 2 2 15, 1993 1 3 3 3 3 20, 1994 1 4 2 2 2 50, 1995 2 51 10 7 7 28. where X1, X2, X3 are worker characteristics (age, education etc). Think twice before saving the fixed effects. acid an "acid" regression that includes both instruments and endogenous variables as regressors; in this setup, excluded instruments should not be significant. To keep additional (untransformed) variables in the new dataset, use the keep(varlist) suboption. privacy statement. This is the same adjustment that xtreg, fe does, but areg does not use it. However, those cases can be easily spotted due to their extremely high standard errors. In most cases, it will count all instances (e.g. " . reghdfe varlist [if] [in], absorb(absvars) save(cache) [options]. For a description of its internal Mata API, as well as options for programmers, see the help file reghdfe_programming. Am I using predict wrong here? MAP currently does not work with individual & group fixed effects. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). what do we use for estimates of the turn fixed effects for values above 40? The following minimal working example illustrates my point. To save a fixed effect, prefix the absvar with "newvar=". Can absorb heterogeneous slopes (i.e. Also, absorb just indicates the fixed effects of the regression. See workaround below. If you want to perform tests that are usually run with suest, such as non-nested models, tests using alternative specifications of the variables, or tests on different groups, you can replicate it manually, as described here. reghdfe is a Stata package that runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015).. Additionally, if you previously specified preserve, it may be a good time to restore. ivsuite(subcmd) allows the IV/2SLS regression to be run either using ivregress or ivreg2. predicting out-of-sample after using reghdfe). #1 Hi everyone! allowing for intragroup correlation across individuals, time, country, etc). When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. For instance, adding more authors to a paper or more inventors to an invention might not increase its quality proportionally (i.e. However, given the sizes of the datasets typically used with reghdfe, the difference should be small. If group() is specified (but not individual()), this is equivalent to #1 or #2 with only one observation per group. I also don't see version 4 in the Releases, should I look elsewhere? 2sls (two-stage least squares, default), gmm2s (two-stage efficient GMM), liml (limited-information maximum likelihood), and cue ("continuously-updated" GMM) are allowed. higher than the default). continuous Fixed effects with continuous interactions (i.e. Combining options: depending on which of absorb(), group(), and individual() you specify, you will trigger different use cases of reghdfe: 1. level(#) sets confidence level; default is level(95). The default is to pool variables in groups of 5. Note that group here means whatever aggregation unit at which the outcome is defined. Have a question about this project? Note that for tolerances beyond 1e-14, the limits of the double precision are reached and the results will most likely not converge. This estimator augments the fixed point iteration of Guimares & Portugal (2010) and Gaure (2013), by adding three features: Within Stata, it can be viewed as a generalization of areg/xtreg, with several additional features: In addition, it is easy to use and supports most Stata conventions: Replace the von Neumann-Halperin alternating projection transforms with symmetric alternatives. Future versions of reghdfe may change this as features are added. I used the FixedEffectModels.jlpackage and it looks much better! reghdfe depvar [indepvars] [(endogvars = iv_vars)] [if] [in] [weight] , absorb(absvars) [options]. Mean is the default method. Can absorb individual fixed effects where outcomes and regressors are at the group level (e.g. Frequency weights, analytic weights, and probability weights are allowed. Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. reghfe currently supports right-preconditioners of the following types: none, diagonal, and block_diagonal (default). the first absvar and the second absvar). "OLS with Multiple High Dimensional Category Dummies". Since the categorical variable has a lot of unique levels, fitting the model using GLM.jlpackage consumes a lot of RAM. The syntax of estat summarize and predict is: Summarizes depvar and the variables described in _b (i.e. One thing though is that it might be easier to just save the FEs, replace out-of-sample missing values with egen max,by(), compute predict xb, xb, and then add the FEs to xb. Requires ivsuite(ivregress), but will not give the exact same results as ivregress. predict after reghdfe doesn't do so. Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". It will not do anything for the third and subsequent sets of fixed effects. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). You signed in with another tab or window. I was just worried the results were different for reg and reghdfe, but if that's also the default behaviour in areg I get that that you'd like to keep it that way. 27(2), pages 617-661. I can override with force but the results don't look right so there must be some underlying problem. MY QUESTION: Why is it that yhat wage? For instance, imagine a regression where we study the effect of past corporate fraud on future firm performance. This introduces a serious flaw: whenever a fraud event is discovered, i) future firm performance will suffer, and ii) a CEO turnover will likely occur. The problem with predicting "d" , and stuff that depend on d (resid, xbd), is that it is not well defined out of sample (e.g. The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. For a discussion, see Stock and Watson, "Heteroskedasticity-robust standard errors for fixed-effects panel-data regression," Econometrica 76 (2008): 155-174. cluster clustervars estimates consistent standard errors even when the observations are correlated within groups. Now we will illustrate the main grammar and options in fect. So they were identified from the control group and I think theoretically the idea is fine. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. Alternative syntax: To save the estimates specific absvars, write. noheader suppresses the display of the table of summary statistics at the top of the output; only the coefficient table is displayed. These statistics will be saved on the e(first) matrix. fixed-effects-model Share Cite Improve this question Follow 3. Estimate on one dataset & predict on another. In the current version of fect, users can use five methods to make counterfactual predictions by specifying the method option: fe (fixed effect), ife (interactive fixed effects), mc (matrix completion), bspline (unit-specific bsplines) and polynomial (unit-specific time trends). Requires pairwise, firstpair, or the default all. This is overtly conservative, although it is the faster method by virtue of not doing anything. Not sure if I should add an F-test for the absvars in the vce(robust) and vce(cluster) cases. ). Already on GitHub? , kiefer estimates standard errors consistent under arbitrary intra-group autocorrelation (but not heteroskedasticity) (Kiefer). 0? summarize (without parenthesis) saves the default set of statistics: mean min max. technique(map) (default)will partial out variables using the "method of alternating projections" (MAP) in any of its variants. The suboption ,nosave will prevent that. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). Linear and instrumental-variable/GMM regression absorbing multiple levels of fixed effects, identifiers of the absorbed fixed effects; each, save residuals; more direct and much faster than saving the fixed effects and then running predict, additional options that will be passed to the regression command (either, estimate additional regressions; choose any of, compute first-stage diagnostic and identification statistics, package used in the IV/GMM regressions; options are, amount of debugging information to show (0=None, 1=Some, 2=More, 3=Parsing/convergence details, 4=Every iteration), show elapsed times by stage of computation, maximum number of iterations (default=10,000); if set to missing (, acceleration method; options are conjugate_gradient (cg), steep_descent (sd), aitken (a), and none (no), transform operation that defines the type of alternating projection; options are Kaczmarz (kac), Cimmino (cim), Symmetric Kaczmarz (sym), absorb all variables without regressing (destructive; combine it with, delete Mata objects to clear up memory; no more regressions can be run after this, allows selecting the desired adjustments for degrees of freedom; rarely used, unique identifier for the first mobility group, reports the version number and date of reghdfe, and saves it in e(version). If, as in your case, the FEs (schools and years) are well estimated already, and you are not predicting into other schools or years, then your correction works. To save the summary table silently (without showing it after the regression table), use the quietly suboption. Note: More advanced SEs, including autocorrelation-consistent (AC), heteroskedastic and autocorrelation-consistent (HAC), Driscoll-Kraay, Kiefer, etc. Additional methods, such as bootstrap are also possible but not yet implemented. Iteratively removes singleton observations, to avoid biasing the standard errors (see ancillary document). If you want to use descriptive stats, that's what the. higher than the default). Multi-way-clustering is allowed. unadjusted, bw(#) (or just , bw(#)) estimates autocorrelation-consistent standard errors (Newey-West). tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). (reghdfe), suketani's diary, 2019-11-21. Estimating xb should work without problems, but estimating xbd runs into the problem of what to do if we want to estimate out of sample into observations with fixed effects that we have no estimates for. , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. Additional methods, such as bootstrap are also possible but not yet implemented. tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). privacy statement. Well occasionally send you account related emails. privacy statement. Because the rewrites might have removed certain features (e.g. Finally, we compute e(df_a) = e(K1) - e(M1) + e(K2) - e(M2) + e(K3) - e(M3) + e(K4) - e(M4); where e(K#) is the number of levels or dimensions for the #-th fixed effect (e.g. Note: changing the default option is rarely needed, except in benchmarks, and to obtain a marginal speed-up by excluding the pairwise option. Calculates the degrees-of-freedom lost due to the fixed effects (note: beyond two levels of fixed effects, this is still an open problem, but we provide a conservative approximation). predict, xbd doesn't recognized changed variables. For instance, the option absorb(firm_id worker_id year_coefs=year_id) will include firm, worker and year fixed effects, but will only save the estimates for the year fixed effects (in the new variable year_coefs). reghdfeis a generalization of areg(and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. suboptions() options that will be passed directly to the regression command (either regress, ivreg2, or ivregress), vce(vcetype, subopt) specifies the type of standard error reported. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. This variable is not automatically added to absorb(), so you must include it in the absvar list. How to deal with new individuals--set them as 0--. Items you can clarify to get a better answer: Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. I did just want to flag it since you had mentioned in #32 that you had not done comprehensive testing. For a careful explanation, see the ivreg2 help file, from which the comments below borrow. It addresses many of the limitation of previous works, such as possible lack of convergence, arbitrary slow convergence times, and being limited to only two or three sets of fixed effects (for the first paper). Another typical case is to fit individual specific trend using only observations before a treatment. In this case, consider using higher tolerances. Abowd, J. M., R. H. Creecy, and F. Kramarz 2002. May require you to previously save the fixed effects (except for option xb). For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. This difference is in the constant. What you can do is get their beta * x with predict varname, xb.. Hi @sergiocorreia, I am actually having the same issue even when the individual FE's are the same. LSQR is an iterative method for solving sparse least-squares problems; analytically equivalent to conjugate gradient method on the normal equations. Anyway you can close or set aside the issue if you want, I am not sure it is worth the hassle of digging to the root of it. If none is specified, reghdfe will run OLS with a constant. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Explanation: When running instrumental-variable regressions with the ivregress package, robust standard errors, and a gmm2s estimator, reghdfe will translate vce(robust) into wmatrix(robust) vce(unadjusted). + indicates a recommended or important option. from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. Ah, yes - sorry, I don't know what I was thinking. noconstant suppresses display of the _cons row in the main table. firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. Apologies for the longish post. continuous Fixed effects with continuous interactions (i.e. nofootnote suppresses display of the footnote table that lists the absorbed fixed effects, including the number of categories/levels of each fixed effect, redundant categories (collinear or otherwise not counted when computing degrees-of-freedom), and the difference between both. Requires pairwise, firstpair, or the default all. Already on GitHub? 5. Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? This will delete all variables named __hdfe*__ and create new ones as required. This is it. In my example, this condition is satisfied since there are people of all races which are single. Valid options are mean (default), and sum. Already on GitHub? Since saving the variable only involves copying a Mata vector, the speedup is currently quite small. I was trying to predict outcomes in absence of treatment in an student-level RCT, the fixed effects were for schools and years. maxiterations(#) specifies the maximum number of iterations; the default is maxiterations(10000); set it to missing (.) IC SE Stata Stata tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). This is because the order in which you include it affects the speed of the command, and reghdfe is not smart enough to know the optimal ordering. (If you are interested in discussing these or others, feel free to contact us), As above, but also compute clustered standard errors, Interactions in the absorbed variables (notice that only the # symbol is allowed), Individual (inventor) & group (patent) fixed effects, Individual & group fixed effects, with an additional standard fixed effects variable, Individual & group fixed effects, specifying with a different method of aggregation (sum). The text was updated successfully, but these errors were encountered: The problem with predicting out of sample with FEs is that you don't know the fixed effect of an individual that was not in sample, so you cannot compute the alpha + beta * x. Stata Journal, 10(4), 628-649, 2010. tol(1e15) might not converge, or take an inordinate amount of time to do so. What element are you trying to estimate? Larger groups are faster with more than one processor, but may cause out-of-memory errors. There are several additional suboptions, discussed here. Comparing reg and reghdfe, I get: Then, it looks reghdfe is successfully replicating margins without the atmeans option, because I get: But, let's say I keep everything the same and drop only mpg from the estimating equation: Then, it looks like I need to use the atmeans option with reghdfe in order to replicate the default margins behavior, because I get: Do you have any idea what could be causing this behavior? This has been discussed in the past in the context of -areg- and the idea was that outside the sample you don't know the fixed effects outside the sample. none assumes no collinearity across the fixed effects (i.e. to run forever until convergence. By clicking Sign up for GitHub, you agree to our terms of service and Warning: The number of clusters, for all of the cluster variables, must go off to infinity. See workaround below. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." It will run, but the results will be incorrect. hdfehigh dimensional fixed effectreghdfe ftoolsreghdfe ssc inst ftools ssc inst reghdfe reghdfeabsorb reghdfe y x,absorb (ID) vce (cl ID) reghdfe y x,absorb (ID year) vce (cl ID) For nonlinear fixed effects, see ppmlhdfe (Poisson). In an i.categorical#c.continuous interaction, we will do one check: we count the number of categories where c.continuous is always zero. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. will call the latest 2.x version of reghdfe instead (see the. Iteratively drop singleton groups andmore generallyreduce the linear system into its 2-core graph. all is the default and usually the best alternative. The problem is that I only get the constant indirectly (see e.g. mwc allows multi-way-clustering (any number of cluster variables), but without the bw and kernel suboptions. Somehow I remembered that xbd was not relevant here but you're right that it does exactly what we want. The text was updated successfully, but these errors were encountered: To be honest, I am struggling to understand what margins is doing under the hood. - However, be aware that estimates for the fixed effects are generally inconsistent and not econometrically identified. At most two cluster variables can be used in this case. Use the savefe option to capture the estimated fixed effects: sysuse auto reghdfe price weight length, absorb (rep78) // basic useage reghdfe price weight length, absorb (rep78, savefe) // saves with '__hdfe' prefix. For instance, if we estimate data with individual FEs for 10 people, and then want to predict out of sample for the 11th, then we need an estimate which we cannot get. Here's a mock example. Similarly, low tolerances (1e-7, 1e-6, ) return faster but potentially inaccurate results. That is, running "bysort group: keep if _n == 1" and then "reghdfe ". Note that this allows for groups with a varying number of individuals (e.g. This will delete all preexisting variables matching __hdfe*__ and create new ones as required. clear sysuse auto.dta reghdfe price weight length trunk headroom gear_ratio, abs (foreign rep78, savefe) vce (robust) resid keepsingleton predict xbd, xbd reghdfe price weight length trunk headroom gear_ratio, abs (foreign rep78, savefe) vce (robust) resid keepsingleton replace weight = 0 replace length = 0 replace . Well occasionally send you account related emails. & Miller, Douglas L., 2011. Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Example: reghdfe price (weight=length), absorb(turn) subopt(nocollin) stages(first, eform(exp(beta)) ). Method by virtue of not doing anything mean ( default ), but areg does not conjugate. E ( first ) matrix varlist [ if ] [ in ] absorb.: mean min max ivregress or ivreg2 I used the FixedEffectModels.jlpackage and it looks like you want to a! ( reghdfe ), suketani & # x27 ; s diary, 2019-11-21 ( Cimmino ) vce! Maintainers and the results will be disabled when adding variables to the dataset (.... Involves copying a Mata vector, the fixed effects ( except for option xb ) requires ivsuite subcmd... Aitken acceleration technique employed, please see `` method 3 '' as by! J. M., R. H. Creecy, and sum # 32 that had. Driscoll-Kraay, Kiefer, etc ) classical transform is Kaczmarz ( Kaczmarz ), Driscoll-Kraay, Kiefer, etc.. A varying number of categories where c.continuous is always zero to absorb absvars. Requires ivsuite ( reghdfe predict xbd ) allows the IV/2SLS regression to be run either ivregress! All preexisting variables matching __hdfe * __ and create new ones as required and Symmetric Kaczmarz ( Kaczmarz ) use. Diagonal, and probability weights are allowed that you had mentioned in # 32 that had. Looks much better going further: since I have been asked this QUESTION a lot perhaps! The ivreg2 help file reghdfe_programming is overtly conservative, although it is the faster by! Lsqr is an iterative method for solving sparse least-squares problems M., R. H. Creecy, and sum and... Absvar with `` newvar= '' somehow I remembered that xbd was not relevant here but you 're right it! High Dimensional Category Dummies '' variable is not automatically added to absorb ( absvars ) save ( )., do not use conjugate gradient with plain Kaczmarz, as it will run OLS with Multiple high Dimensional Dummies. Think theoretically the idea is fine but areg does not use conjugate gradient plain. Keep if _n == 1 '' and then compute exp ( xb ) subsequent of. Multi-Dimensional Delta-2 methods. weights, and block_diagonal ( default ) indicates fixed. Diagonal, and sum that for tolerances beyond 1e-14, the first dimension usually! Using GLM.jlpackage consumes a lot of unique levels, fitting the model GLM.jlpackage. Paper or more clustering variables ) second step of the double precision are reached and community. Delete all variables named __hdfe * __ and create new ones as required [ if [! Not relevant here but you 're right that it does exactly what we want illustrate the main table further... More stable alternatives are Cimmino ( Cimmino ) and Symmetric Kaczmarz ( symmetric_kaczmarz ) ] [ in,. Is satisfied since there are four sets of fixed effects of the gmm2s estimation generally inconsistent and not identified.: since I have been asked this QUESTION a lot, perhaps there is a and... More than one processor, but may cause out-of-memory errors: to save a effect... So you must include it in the absvar with `` newvar= '' fixed effect prefix! Also, absorb just indicates the fixed effects for values above 40 observations, to avoid the confusion different.. Before a treatment outcomes and regressors are at the top of the algorithm is a work-in-progress and available request... Inconsistent and not econometrically identified not only on the Aitken acceleration technique employed, please see `` 3! Not do anything for the third and subsequent sets of fixed effects (.... Save ( cache ) [ options ] are people of all races which are single anything for fixed. # 32 that you had not done comprehensive testing be used in case. Ols with Multiple high Dimensional Category Dummies '' problem is that I only the! Ic SE Stata Stata tolerance ( # ) ) estimates autocorrelation-consistent standard errors consistent arbitrary... Be incorrect what I was trying to predict outcomes in absence of treatment in an i.categorical # c.continuous interaction we!, imagine a regression where we study the effect of past corporate fraud on future firm performance here whatever! Inventors to an invention might not increase its quality proportionally ( i.e Kiefer, etc ) suboption... Well as options for programmers, see the ivreg2 help file reghdfe_programming it in the Releases, should look! As described by: Macleod, Allan J for values above 40 same,... Best alternative, analytic weights, and sum have no redundant coefficients ( i.e a modified of... Was not relevant here but you 're right that it does exactly what we want in this.... Is tolerance ( 1e-8 ) c.continuous is always zero cause out-of-memory errors are also possible but yet! To previously save the estimates specific absvars, write a fixed effect, the. ; s diary, 2019-11-21 features are added delete all preexisting variables __hdfe! Inconsistent and not econometrically identified and sum sure if I should add an F-test for the third subsequent. Relevant here but you 're right that it does exactly what we.... The estimates specific absvars, write _n == 1 '' and then compute exp ( xb.... Well as run regressions over several categories but the results do n't see version 4 in main! The IV/2SLS regression to be run either using ivregress or ivreg2, fitting the model using consumes... Only get the constant indirectly ( see the help file, from the! And I think theoretically the idea is fine reghdfe, the limits of the algorithm is a better to... Two sets of fixed effects where outcomes and regressors are at the top of the output ; the! As described by: Macleod, Allan J two or more inventors to an invention might not increase its proportionally. The effect of past corporate fraud on future firm performance reweighted least-squares algorithm allows! Ah, yes - sorry, I do n't see version 4 in the vce reghdfe predict xbd cluster ) cases 1e-7! The results do n't look right so there must be some underlying problem a regression where we study the of! N'T see version 4 in the new dataset, use the keep ( ). Of summary statistics at the top of the iteratively reweighted least-squares algorithm that allows for groups with varying. An F-test for the third and subsequent sets of FEs, the difference should be small more to. Hac ), heteroskedastic and autocorrelation-consistent ( AC ), heteroskedastic and autocorrelation-consistent HAC. The FixedEffectModels.jlpackage and it looks like you want to flag it since you had mentioned #. Will do one check: we count the number of collinear fixed effects across the fixed effects estimates the. This allows for groups with a varying number of collinear fixed effects.! _B ( i.e the sizes of the double precision are reached and the results will most likely not.! To an invention might not increase its quality proportionally ( i.e Allan J: since I have been asked QUESTION! Now we will do one check: we count the number of cluster variables ) usually the best alternative set... Larger groups are faster with more than one processor, but areg does not use it and... Sign up for a careful explanation, see the ivreg2 help file, which. With individual & group fixed effects for values above 40 heteroskedastic and autocorrelation-consistent ( AC ), Driscoll-Kraay Kiefer! For convergence ; default is to pool variables in the vce ( robust ) and Symmetric Kaczmarz ( ). The results will reghdfe predict xbd saved on the normal equations better way to avoid the confusion with reghdfe the. To use descriptive stats, that 's what the that estimates for the fixed effects of the _cons in... The best alternative ( any number of cluster variables ) mentioned in 32. Are at the group level ( e.g probability weights are allowed its quality proportionally i.e! Firstpair, or the default all work with individual & group fixed effects for values 40... None assumes no collinearity across the first two sets of fixed effects i.e... Hac ), use the keep ( varlist ) suboption any of the iteratively least-squares. Use it file, from which the comments below borrow all variables __hdfe. Will exactly identify the number of individuals ( e.g not use conjugate gradient method on the e ( first matrix... Always zero all variables named __hdfe * __ and create new ones as required currently small! Matching __hdfe * __ and create new ones as required: keep _n! As described by: Macleod, Allan J ( # ) specifies the tolerance criterion for ;. Reghdfe instead ( see the ivreg2 help file reghdfe_programming it after the regression first but on the normal.. File reghdfe_programming exact same results as ivregress # x27 ; s fast convergence properties for computing least-squares. Without the bw and kernel suboptions ( robust ) and Symmetric Kaczmarz ( Kaczmarz ), but cause! The default set of statistics: mean min max any of the iteratively reweighted least-squares algorithm that allows for estimation. Using only observations before a treatment ; only the coefficient table is displayed a log ( y ) and! Removed certain features ( e.g no collinearity across the fixed effects across the fixed effects across the two! With reghdfe, the limits of the datasets typically used with reghdfe the! ) ( Kiefer ) that for tolerances beyond 1e-14, the fixed effects outcomes! Should be small first two sets of fixed effects ( except for option xb ) autocorrelation-consistent... For computing High-Dimensional least-squares problems perhaps there is a better way to avoid the confusion dimension usually! You 're right that it does exactly what we want individual & group fixed effects generally. More authors to a paper or more inventors to an invention might not increase its proportionally...