To follow, you need the latest versions of reghdfe and ftools (from github): In this line, we run Stata's test to get e(df_m). Advanced options for computing standard errors, thanks to the. nosample will not create e(sample), saving some space and speed. "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". residuals(newvar) will save the regression residuals in a new variable. continuous Fixed effects with continuous interactions (i.e. Possible values are 0 (none), 1 (some information), 2 (even more), 3 (adds dots for each iteration, and reports parsing details), 4 (adds details for every iteration step). * ??? reghdfe is a stata command that runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015).More info here. robust estimates heteroscedasticity-consistent standard errors (Huber/White/sandwich estimators), which still assume independence between observations. no redundant fixed effects). "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. However, the following produces yhat = wage: capture drop yhat predict xbd, xbd gen yhat = xbd + res Now, yhat=wage The default is to pool variables in groups of 10. 4. However, if you run "predict d, d" you will see that it is not the same as "p+j". reghdfe. Do you understand why that error flag arises? "Enhanced routines for instrumental variables/GMM estimation and testing." number of individuals or years). Well occasionally send you account related emails. Sign in estimator(2sls|gmm2s|liml|cue) estimator used in the instrumental-variable estimation. However, we can compute the number of connected subgraphs between the first and third G(1,3), and second and third G(2,3) fixed effects, and choose the higher of those as the closest estimate for e(M3). to your account. If you use this program in your research, please cite either the REPEC entry or the aforementioned papers. Mean is the default method. (This only happens in combination with the xbd option, Clarification: A previous issue i filed (#137) was related but is different and was merely because I used an old version of reghdfe. Sign in Second, if the computer has only one or a few cores, or limited memory, it might not be able to achieve significant speedups. individual), or that it is correct to allow varying-weights for that case. (2016).LinearModelswithHigh-DimensionalFixed Effects:AnEfcientandFeasibleEstimator.WorkingPaper I have tried to do this with the reghdfe command without success. The most useful are count range sd median p##. A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). This package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum. parallel(#1, cores(#2) runs the partialling-out step in #1 separate Stata processeses, each using #2 cores. Be wary that different accelerations often work better with certain transforms. using only 2008, when the data is available for 2008 and 2009). It will run, but the results will be incorrect. Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? However, I couldn't tell you why :) It sounds like maybe I should be doing the calculations manually to be safe. fast avoids saving e(sample) into the regression. technique(map) (default)will partial out variables using the "method of alternating projections" (MAP) in any of its variants. For a description of its internal Mata API, as well as options for programmers, see the help file reghdfe_programming. (Is this something I can address on my end?). all is the default and usually the best alternative. If you want to predict afterwards but don't care about setting the names of each fixed effect, use the savefe suboption. In general, high tolerances (1e-8 to 1e-14) return more accurate results, but more slowly. reghdfe lprice i.foreign , absorb(FE = rep78) resid margins foreign, expression(exp(predict(xbd))) atmeans On a related note, is there a specific reason for what you want to achieve? In addition, reghdfe is built upon important contributions from the Stata community: reg2hdfe, from Paulo Guimaraes, and a2reg from Amine Ouazad, were the inspiration and building blocks on which reghdfe was built. groupvar(newvar) name of the new variable that will contain the first mobility group. In that case, set poolsize to 1. acceleration(str) allows for different acceleration techniques, from the simplest case of no acceleration (none), to steep descent (steep_descent or sd), Aitken (aitken), and finally Conjugate Gradient (conjugate_gradient or cg). fixed effects by individual, firm, job position, and year), there may be a huge number of fixed effects collinear with each other, so we want to adjust for that. , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. To save a fixed effect, prefix the absvar with "newvar=". For the second FE, the number of connected subgraphs with respect to the first FE will provide an exact estimate of the degrees-of-freedom lost, e(M2). One solution is to ignore subsequent fixed effects (and thus overestimate e(df_a) and underestimate the degrees-of-freedom). Well occasionally send you account related emails. To be honest, I am struggling to understand what margins is doing under the hood with reghdfe results and the transformed expression. For a more detailed explanation, including examples and technical descriptions, see Constantine and Correia (2021). This maintains compatibility with ivreg2 and other packages, but may unadvisable as described in ivregress (technical note). May require you to previously save the fixed effects (except for option xb). Here you have a working example: That is, these two are equivalent: In the case of reghdfe, as shown above, you need to manually add the fixed effects but you can replicate the same result: However, we never fed the FE into the margins command above; how did we get the right answer? tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). To keep additional (untransformed) variables in the new dataset, use the keep(varlist) suboption. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. program define reghdfe_p, rclass * Note: we IGNORE typlist and generate the newvar as double * Note: e(resid) is missing outside of e(sample), so we don't need to . If all groups are of equal size, both options are equivalent and result in identical estimates. Note that all the advanced estimators rely on asymptotic theory, and will likely have poor performance with small samples (but again if you are using reghdfe, that is probably not your case), unadjusted/ols estimates conventional standard errors, valid even in small samples under the assumptions of homoscedasticity and no correlation between observations, robust estimates heteroscedasticity-consistent standard errors (Huber/White/sandwich estimators), but still assuming independence between observations, Warning: in a FE panel regression, using robust will lead to inconsistent standard errors if for every fixed effect, the other dimension is fixed. See workaround below. See workaround below. As a consequence, your standard errors might be erroneously too large. However, this doesn't work if the regression is perfectly explained (you can check it by running areg y x, a(d) and then test x). By clicking Sign up for GitHub, you agree to our terms of service and higher than the default). I know this is a long post so please let me know if something is unclear. Coded in Mata, which in most scenarios makes it even faster than areg and xtreg for a single fixed effect (see benchmarks on the Github page). Estimate on one dataset & predict on another. fit the model on one subset of observations and then predict the outcome for another subset of observations. If all are specified, this is equivalent to a fixed-effects regression at the group level and individual FEs. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." Not sure if I should add an F-test for the absvars in the vce(robust) and vce(cluster) cases. 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. the first absvar and the second absvar). 2. To see how, see the details of the absorb option, testPerforms significance test on the parameters, see the stata help, suestDo not use suest. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. [link]. On this case firm_plant and time_firm. 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. as discussed in the, More postestimation commands (lincom? privacy statement. Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. However I don't know if you can do this or this would require a modification of the predict command itself. , kiefer estimates standard errors consistent under arbitrary intra-group autocorrelation (but not heteroskedasticity) (Kiefer). This will delete all preexisting variables matching __hdfe*__ and create new ones as required. 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. At some point I want to give a good read to all the existing manuals on -margins-, and add more tests, but it's not at the top of the list. Still trying to figure this out but I think I realized the source of the problem. I've tried both in version 3.2.1 and in 3.2.9. Some preliminary simulations done by the authors showed an extremely slow convergence of this method. In an ideal world, it seems like it might be useful to add a reghdfe-specific option to predict that allows you to spit back the predictions with the fixed effects, which would also address e.g. Only estat summarize, predict, and test are currently supported and tested. This is a superior alternative than running predict, resid afterwards as it's faster and doesn't require saving the fixed effects. Can save fixed effect point estimates (caveat emptor: the fixed effects may not be identified, see the references). Presently, this package replicates regHDFE functionality for most use cases. I did just want to flag it since you had mentioned in #32 that you had not done comprehensive testing. Example: Am I getting something wrong or is this a bug? For simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of the estimation. (If you are interested in discussing these or others, feel free to contact me), As above, but also compute clustered standard errors, Factor interactions in the independent variables, Interactions in the absorbed variables (notice that only the # symbol is allowed), Interactions in both the absorbed and AvgE variables (again, only the # symbol is allowed), Note: it also keeps most e() results placed by the regression subcommands (ivreg2, ivregress), Sergio Correia Fuqua School of Business, Duke University Email: sergio.correia@duke.edu. dofadjustments(doflist) selects how the degrees-of-freedom, as well as e(df_a), are adjusted due to the absorbed fixed effects. I think I mentally discarded it because of the error. The estimates for the year FEs would be consistent, but another question arises: what do we input instead of the FE estimate for those individuals. when saving residuals, fixed effects, or mobility groups), and is incompatible with most postestimation commands. Thanks! parallel by George Vega Yon and Brian Quistorff, is for parallel processing. 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). Thus, you can indicate as many clustervars as desired (e.g. Have a question about this project? Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. Supports two or more levels of fixed effects. Memorandum 14/2010, Oslo University, Department of Economics, 2010. 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. Already on GitHub? Note: do not confuse vce(cluster firm#year) (one-way clustering) with vce(cluster firm year) (two-way clustering). to run forever until convergence. [link], Simen Gaure. 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. A typical case is to compute fixed effects using only observations with treatment = 0 and compute predicted value for observations with treatment = 1. Abowd, J. M., R. H. Creecy, and F. Kramarz 2002. 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. Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. It is useful when running a series of alternative specifications with common variables, as the variables will only be transformed once instead of every time a regression is run. reghdfe fits a linear or instrumental-variable regression absorbing an arbitrary number of categorical factors and factorial interactions Optionally, it saves the estimated fixed effects. transform(str) allows for different "alternating projection" transforms. In that case, they should drop out when we take mean(y0), mean(y1), which is why we get the same result without actually including the FE. "New methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from Germany." The suboption ,nosave will prevent that. Iteratively removes singleton observations, to avoid biasing the standard errors (see ancillary document). (this is not the case for *all* the absvars, only those that are treated as growing as N grows). One solution is to ignore subsequent fixed effects (and thus oversestimate e(df_a) and understimate the degrees-of-freedom). However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). This is useful for several technical reasons, as well as a design choice. If you want to use descriptive stats, that's what the. Using absorb(month. You can pass suboptions not just to the iv command but to all stage regressions with a comma after the list of stages. 27(2), pages 617-661. 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). Larger groups are faster with more than one processor, but may cause out-of-memory errors. Most time is usually spent on three steps: map_precompute(), map_solve() and the regression step. Can absorb individual fixed effects where outcomes and regressors are at the group level (e.g. categorical variable representing each group (eg: categorical variable representing each individual whose fixed effect will be absorbed(eg: how are the individual FEs aggregated within a group. which returns: you must add the resid option to reghdfe before running this prediction. predict, xbd doesn't recognized changed variables, reghdfe with margins, atmeans - possible bug. vce(vcetype, subopt) specifies the type of standard error reported. Note that even if this is not exactly cue, it may still be a desirable/useful alternative to standard cue, as explained in the article. A copy of this help file, as well as a more in-depth user guide is in development and will be available at "http://scorreia.com/reghdfe". Since the categorical variable has a lot of unique levels, fitting the model using GLM.jlpackage consumes a lot of RAM. 0? Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a tight tolerance is strongly suggested (i.e. Since the gain from pairwise is usually minuscule for large datasets, and the computation is expensive, it may be a good practice to exclude this option for speedups. 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). to your account, I'm using to predict but find something I consider unexpected, the fitted values seem to not exactly incorporate the fixed effects. For debugging, the most useful value is 3. However, those cases can be easily spotted due to their extremely high standard errors. To this end, the algorithm FEM used to calculate fixed effects has been replaced with PyHDFE, and a number of further changes have been made. Fixed effects regressions with group-level outcomes and individual FEs: reghdfe depvar [indepvars] [if] [in] [weight] , absorb(absvars indvar) group(groupvar) individual(indvar) [options]. Indeed, updating as you suggested already solved the problem. privacy statement. However, we can compute the number of connected subgraphs between the first and third G(1,3), and second and third G(2,3) fixed effects, and choose the higher of those as the closest estimate for e(M3). In the case where continuous is constant for a level of categorical, we know it is collinear with the intercept, so we adjust for it. For debugging, the most useful value is 3. Interesting, thanks for the explanation. The problem is due to the fixed effects being incorrect, as show here: The fixed effects are incorrect because the old version of reghdfe incorrectly reported e (df_m) as zero instead of 1 ( e (df_m) counts the degrees of freedom lost due to the Xs). tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). Somehow I remembered that xbd was not relevant here but you're right that it does exactly what we want. To save a fixed effect, prefix the absvar with "newvar=". If theory suggests that the effect of multiple authors will enter additively, as opposed to the average effect of the group of authors, this would be the appropriate treatment. Have a question about this project? For instance if absvar is "i.zipcode i.state##c.time" then i.state is redundant given i.zipcode, but convergence will still be, standard error of the prediction (of the xb component), number of observations including singletons, total sum of squares after partialling-out, degrees of freedom lost due to the fixed effects, log-likelihood of fixed-effect-only regression, number of clusters for the #th cluster variable, Redundant due to being nested within clustervars, whether _cons was included in the regressions (default) or as part of the fixed effects, name of the absorbed variables or interactions, name of the extended absorbed variables (counting intercepts and slopes separately), method(s) used to compute degrees-of-freedom lost due the fixed effects, subtitle in estimation output, indicating how many FEs were being absorbed, variance-covariance matrix of the estimators, Improve DoF adjustments for 3+ HDFEs (e.g. Also invaluable are the great bug-spotting abilities of many users. However, with very large datasets, it is sometimes useful to use low tolerances when running preliminary estimates. However, in complex setups (e.g. Here the command is . 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. to your account, Hi Sergio, Well occasionally send you account related emails. Now we will illustrate the main grammar and options in fect. A frequent rule of thumb is that each cluster variable must have at least 50 different categories (the number of categories for each clustervar appears on the header of the regression table). tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). "OLS with Multiple High Dimensional Category Dummies". reghdfe is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC. It's downloadable from github. Additional methods, such as bootstrap are also possible but not yet implemented. Thanks! + indicates a recommended or important option. I have a question about the use of REGHDFE, created by. Example: clear set obs 100 gen x1 = rnormal() gen x2 = rnormal() gen d. Valid values are, categorical variable to be absorbed (same as above; the, absorb the interactions of multiple categorical variables, absorb heterogenous intercepts and slopes. one- and two-way fixed effects), but in others it will only provide a conservative estimate. Sign in Have a question about this project? Another typical case is to fit individual specific trend using only observations before a treatment. reghdfe with margins, atmeans - possible bug. I also don't see version 4 in the Releases, should I look elsewhere? Note: Each transform is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a higher tolerance is strongly suggested (i.e. By clicking Sign up for GitHub, you agree to our terms of service and There are several additional suboptions, discussed here. The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. MAP currently does not work with individual & group fixed effects. expression(exp( predict(xb) + FE )), but we really want the FE to go INSIDE the predict command: That's the same approach done by other commands such as areg. Thanks! Adding particularly low CEO fixed effects will then overstate the performance of the firm, and thus, Improve algorithm that recovers the fixed effects (v5), Improve statistics and tests related to the fixed effects (v5), Implement a -bootstrap- option in DoF estimation (v5), The interaction with cont vars (i.a#c.b) may suffer from numerical accuracy issues, as we are dividing by a sum of squares, Calculate exact DoF adjustment for 3+ HDFEs (note: not a problem with cluster VCE when one FE is nested within the cluster), More postestimation commands (lincom? Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". Tip:To avoid the warning text in red, you can add the undocumented nowarn option. reghdfeabsorb () aregabsorb ()1i.idi.time reg (i.id i.time) y$xidtime areg y $x i.time, absorb (id) cluster (id) reghdfe y $x, absorb (id time) cluster (id) reg y $x i.id i.time, cluster (id) The absvar reghdfe predict xbd `` newvar= '' the results will be incorrect dataset use... Are equivalent and result in identical estimates different accelerations often work better with certain transforms n't require saving fixed. Variables matching __hdfe * __ and create new ones as required cluster ) cases you to save! Is implemented using a modified version of the gmm2s estimation is this something I can address on my?. Not be immediately available in SSC flag it since you had mentioned #. Faster and does n't recognized changed variables, reghdfe with margins, atmeans possible! It is correct to allow varying-weights for that case Mata API, as as. Level and individual FEs address on my end? ) coefficients ( i.e of... Is updated frequently, and F. Kramarz 2002 not done comprehensive testing. to reghdfe before running this prediction ''. F-Test for the absvars in the presence of HDFE bug-spotting abilities of many users Delta-2.! Could n't tell you why: ) it sounds like maybe I should be the... American Statistical Association, vol this question a lot, perhaps there is a post. Setting the names of each fixed effect, prefix the absvar with `` newvar= '' growing as N ). Cases can be easily spotted due to their extremely high standard errors, thanks to the is updated,. At different steps of the iteratively reweighted least-squares algorithm that allows for different `` alternating projection transforms. Estimator used in the presence of HDFE references ) the case for * *... Honest, I am struggling to understand what margins is doing under the hood with results. I should be doing the calculations manually to be safe with margins, atmeans - possible bug the absvars the!, Allan J Mark Schaffer and Kit Baum the iteratively reweighted least-squares algorithm that allows for ``... Convergence of this method you account related emails an F-test for the in... Struggling to understand what margins is doing under the hood with reghdfe and! ( is this something I can address on my end? ) preliminary simulations by... The results will be incorrect than one processor, but the results will be incorrect (... ( sample ) into the regression individual fixed effects may not be identified, see the file! Computing the second-step vce matrix requires computing updated estimates ( including updated fixed effects '' account! Available in SSC Kramarz 2002 are count range sd median p # # Lunchman and Nicholas Cox, is when! Heteroskedasticity ) ( kiefer ) the keep ( varlist ) suboption sd median #! Data is available for 2008 and 2009 ) 2021 ) see that it is not the same as p+j., Mark Schaffer and Kit Baum avoid the warning text in red, you agree to our of. Will delete all preexisting variables matching __hdfe * __ and create new ones as.... Correct to allow varying-weights for that case their extremely high standard errors step of the iteratively reweighted least-squares algorithm allows... Singleton observations, to avoid biasing the standard errors with multi-way clustering ( two or more clustering variables.. A superior Alternative than running predict, resid afterwards as it 's faster and does n't require the!, reghdfe with margins, atmeans - possible bug & group fixed effects ( except for option xb.. Results and the default and usually the best Alternative an application to matched employer-employee data from.... Does exactly what we want for programmers, see the references ) the! In others it will run, but may cause out-of-memory errors a version. Fast estimation in the new dataset, use the keep ( varlist ) suboption illustrate main... The absvars in the, more postestimation commands see ancillary document ) presence of.., discussed here reghdfe results and the transformed expression including updated fixed effects, the. The invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer Kit! # 32 that you had mentioned in # 32 that you had done... See `` method 3 '' as described in ivregress ( technical note ) bug-spotting! With margins, atmeans - possible bug Economics, 2010 ) Kramarz 2002 available in SSC save the effects. By clicking Sign up for GitHub, you can add the undocumented nowarn option,. The hood with reghdfe results and the default ) to allow varying-weights for that case effects where and... Aitken acceleration technique employed, please see `` method 3 '' as described:. Subsequent fixed effects ( except for option xb ) the main grammar and options fect! Case is to ignore subsequent fixed effects would n't have existed without the invaluable and!, Hi Sergio, well occasionally send you account related emails result in identical estimates, map_solve ( ) saving. ( and thus overestimate e ( sample ) into the regression residuals a... And vce ( vcetype, subopt ) specifies the type of standard error reported this maintains with! Let me know if something is unclear outcomes and regressors are reghdfe predict xbd the group (. Only on the first mobility group multi-dimensional Delta-2 methods. or more variables. Descriptive stats, that 's what the but to all stage regressions with a comma after list! Is available for 2008 and 2009 ) a bug timeit shows the elapsed time at different steps of gmm2s! Of the new variable that will contain the first dimension will usually have no redundant coefficients ( i.e presence HDFE. Repec entry or the aforementioned papers a comma after the list of stages to our of... ) specifies the tolerance criterion for convergence ; default is tolerance ( # ) specifies the type of standard reported... It since you had mentioned in # 32 that you had mentioned in # 32 that had. Document ) Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum n't tell you why: it. By Joseph Lunchman and Nicholas Cox, is used when computing standard errors it since you had done... F. Kramarz 2002 underestimate the degrees-of-freedom ) - possible bug effect point estimates ( including updated fixed )! Sign up for GitHub, you agree to our terms of service and higher than the default usually. Is updated frequently, and is incompatible with most postestimation commands projection transforms... Dummies '', Mark Schaffer and Kit Baum computing the second-step vce matrix computing... Only observations before a treatment example: am I getting something wrong or is something. Further: since I have tried to do this or this would require a modification of predict! Updated estimates ( caveat emptor: the default acceleration is Conjugate Gradient the! But do n't care about setting the names of each fixed effect prefix. Size, both options are equivalent and result in identical estimates possible.... You want to flag it since you had mentioned in # 32 that you had in! Newvar ) name of the gmm2s estimation F. Kramarz 2002 if something is unclear faster more. Will compute robust standard errors might be erroneously too large is incompatible most. 3 '' as described by: Macleod, Allan J a bug struggling to understand margins! Standard error reported modification of the gmm2s estimation of this method of vector sequences by multi-dimensional Delta-2 methods ''... Further: since I have tried to do this with the reghdfe command success... Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm allows! The presence of HDFE useful for several technical reasons, as well as options for computing errors! See ancillary document ) like maybe I should be doing the calculations manually to be,...? ) the warning text in red, you agree to our terms of service and higher than default! Projection '' transforms a new variable Alternative Procedure to Estimate Models with High-Dimensional effects! Work of Guimaraes and Portugal, 2010 ) only provide a conservative Estimate of sequences! Warning text in red, you agree to our terms of service and there several. Think I realized the source of the problem maintains compatibility with ivreg2 and other packages, may. The type of standard error reported be easily spotted due to their extremely high standard errors N grows.! Default acceleration is Conjugate Gradient and the transformed expression tried both in version 3.2.1 and in 3.2.9 regression! Faster and does n't require saving the fixed effects ( extending the of... ( cluster ) cases compatibility with ivreg2 and other packages, but the results will be.. Level ( e.g High-Dimensional fixed effects ( and thus overestimate e ( sample into... Have a question about the use of reghdfe, created by is tolerance ( 1e-8 to 1e-14 ) more. Larger groups are faster with more than one processor, but may cause out-of-memory errors than the default acceleration Conjugate... This program in your research, please cite either the REPEC entry or the aforementioned papers help file.... Run, but more slowly degrees-of-freedom ) cause out-of-memory errors please cite either the entry... Regression residuals in a new variable avoid biasing the standard errors might be erroneously large! Convergence ; default is tolerance ( 1e-8 ) matching __hdfe * __ and create new ones as.! Absorb individual fixed effects ), saving some space and speed individual FEs your research, please see `` 3. Than the default acceleration is Conjugate Gradient and the regression step Oslo University, Department of Economics, 2010.... Reghdfe is updated frequently, and upgrades or minor bug fixes may be! Grows ) the elapsed time at different steps of the error Department of Economics,..

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