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. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. this issue: #138. For diagnostics on the fixed effects and additional postestimation tables, see sumhdfe. See workaround below. here. residuals (without parenthesis) saves the residuals in the variable _reghdfe_resid (overwriting it if it already exists). ffirst compute and report first stage statistics (details); requires the ivreg2 package. 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). Please be aware that in most cases these estimates are neither consistent nor econometrically identified. individual slopes, instead of individual intercepts) are dealt with differently. 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. Not sure if I should add an F-test for the absvars in the vce(robust) and vce(cluster) cases. 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). individual(indvar) categorical variable representing each individual (eg: inventor_id). Note that parallel() will only speed up execution in certain cases. from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. In addition, reghdfe is build 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. reghfe currently supports right-preconditioners of the following types: none, diagonal, and block_diagonal (default). predictnl pred_prob=exp (predict (xbd))/ (1+exp (predict (xbd))) , se (pred_prob_se) Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. For more than two sets of fixed effects, there are no known results that provide exact degrees-of-freedom as in the case above. With the reg and predict commands it is possible to make out-of-sample predictions, i.e. By clicking Sign up for GitHub, you agree to our terms of service and predict u_hat0, xbd My questions are as follow 1) Does it give sense to predict the fitted values including the individual effects (as indicated above) to estimate the mean impact of the technology by taking the difference of predicted values (u_hat1-u_hat0)? Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). Performance is further enhanced by some new techniques we . In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). absorb() is required. 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), degrees of freedom lost due to the fixed effects, log-likelihood of fixed-effect-only regression, number of clusters for the #th cluster variable, Number of categories of the #th absorbed FE, Number of redundant categories of the #th absorbed FE, names of endogenous right-hand-side variables, name of the absorbed variables or interactions, variance-covariance matrix of the estimators. -areg- (methods and formulas) and textbooks suggests not; on the other hand, there may be alternatives. one patent might be solo-authored, another might have 10 authors). 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. I believe the issue is that instead, the results of predict(xb) are being averaged and THEN the FE is being added for each observation. prune(str)prune vertices of degree-1; acts as a preconditioner that is useful if the underlying network is very sparse; currently disabled. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. In contrast, other production functions might scale linearly in which case "sum" might be the correct choice. I try to estimate the predicted probability after a regression of the log odds ratio on covariates and many fixed effects. 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. 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. no redundant fixed effects). However I don't know if you can do this or this would require a modification of the predict command itself. one- and two-way fixed effects), but in others it will only provide a conservative estimate. By clicking Sign up for GitHub, you agree to our terms of service and For instance, adding more authors to a paper or more inventors to an invention might not increase its quality proportionally (i.e. Already on GitHub? 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. reghdfe is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC. (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). It will run, but the results will be incorrect. Larger groups are faster with more than one processor, but may cause out-of-memory errors. display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] Estimation options. groupvar(newvar) name of the new variable that will contain the first mobility group. version(#) reghdfe has had so far two large rewrites, from version 3 to 4, and version 5 to version 6. However, given the sizes of the datasets typically used with reghdfe, the difference should be small. The problem is that margins flags this as a problem with the error "expression is a function of possibly stochastic quantities other than e(b)". You can check their respective help files here: reghdfe3, reghdfe5. Already on GitHub? For more information on the algorithm, please reference the paper, technique(gt) variation of Spielman et al's graph-theoretical (GT) approach (using a spectral sparsification of graphs); currently disabled. That makes sense. Iteratively drop singleton groups andmore generallyreduce the linear system into its 2-core graph. That behavior only works for xb, where you get the correct results. Can absorb heterogeneous slopes (i.e. Items you can clarify to get a better answer: Am I using predict wrong here? allowing for intragroup correlation across individuals, time, country, etc). technique(map) (default)will partial out variables using the "method of alternating projections" (MAP) in any of its variants. To keep additional (untransformed) variables in the new dataset, use the keep(varlist) suboption. 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). Additional features include: 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). This is overtly conservative, although it is the faster method by virtue of not doing anything. Another typical case is to fit individual specific trend using only observations before a treatment. tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). Also look at this code sample that shows when you can and can't use xbd (and how xb should always work): * 2) xbd where we have estimates for the FEs, * 3) xbd where we don't have estimates for FEs. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. Thanks! to run forever until convergence. none assumes no collinearity across the fixed effects (i.e. Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. predict xbd, xbd The summary table is saved in e(summarize). residuals(newvar) will save the regression residuals in a new variable. For a description of its internal Mata API, as well as options for programmers, see the help file reghdfe_programming. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. Also invaluable are the great bug-spotting abilities of many users. The text was updated successfully, but these errors were encountered: Would it make sense if you are able to only predict the -xb- part? fixed-effects-model Share Cite Improve this question Follow Each clustervar permits interactions of the type var1#var2 (this is faster than using egen group() for a one-off regression). I am running the following commands: Code: reghdfe log_odds_ratio depvar [pw=weights], absorb (year county_fe) cluster (state) resid predictnl pred_prob=exp (predict (xbd))/ (1+exp (predict (xbd))) , se (pred_prob_se) Similarly, it makes sense to compute predictions for switchers, but not for individuals that are always treated. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. reghdfe now permits estimations that include individual fixed effects with group-level outcomes. Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? preconditioner(str) LSMR/LSQR require a good preconditioner in order to converge efficiently and in few iterations. 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. Kind regards, Carlo (Stata 17.0 SE) Alberto Alvarez Join Date: Jul 2016 Posts: 191 #5 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). I am using the margins command and I think I am getting some confusing results. 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). summarize(stats) will report and save a table of summary of statistics of the regression variables (including the instruments, if applicable), using the same sample as the regression. the first absvar and the second absvar). 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). 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). The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. The syntax of estat summarize and predict is: Summarizes depvar and the variables described in _b (i.e. However, if you run "predict d, d" you will see that it is not the same as "p+j". groupvar(newvar) name of the new variable that will contain the first mobility group. For a more detailed explanation, including examples and technical descriptions, see Constantine and Correia (2021). Valid options are mean (default), and sum. commands such as predict and margins.1 By all accounts reghdfe represents the current state-of-the-art command for estimation of linear regression models with HDFE, and the package has been very well accepted by the academic community.2 The fact that reghdfeoers a very fast and reliable way to estimate linear regression its citations), so using "mean" might be the sensible choice. no redundant fixed effects). avar by Christopher F Baum and Mark E Schaffer, is the package used for estimating the HAC-robust standard errors of ols regressions. 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? regressors with different coefficients for each FE category), 3. ivreg2, by Christopher F Baum, Mark E Schaffer, and Steven Stillman, is the package used by default for instrumental-variable regression. not the excluded instruments). Since reghdfe currently does not allow this, the resulting standard errors will not be exactly the same as with ivregress. higher than the default). Most time is usually spent on three steps: map_precompute(), map_solve() and the regression step. Additionally, if you previously specified preserve, it may be a good time to restore. For instance, vce(cluster firm year) will estimate SEs with firm and year clustering (two-way clustering). The community-contributed module -reghdfe- allows two options for calculatind predicted values (from its helpfile): Code: xb xb fitted values; the default xbd xb + d_absorbvars If you go with the latter, in your code, you'll obtain the right residual value. Indeed, updating as you suggested already solved the problem. I used the FixedEffectModels.jlpackage and it looks much better! Another case is to add additional individuals during the same years. 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. In your case, it seems that excluding the FE part gives you the same results under -atmeans-. continuous Fixed effects with continuous interactions (i.e. A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). Time-varying executive boards & board members. 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. Note that for tolerances beyond 1e-14, the limits of the double precision are reached and the results will most likely not converge. individual slopes, instead of individual intercepts) are dealt with differently. In other words, an absvar of var1##c.var2 converges easily, but an absvar of var1#c.var2 will converge slowly and may require a tighter tolerance. I can't figure out how to actually implement this expression using predict, though. It is equivalent to dof(pairwise clusters continuous). Moreover, after fraud events, the new CEOs are usually specialized in dealing with the aftershocks of such events (and are usually accountants or lawyers). You can browse but not post. e(M1)==1), since we are running the model without a constant. reghdfe dep_var ind_vars, absorb(i.fixeff1 i.fixeff2, savefe) cluster(t) resid My attempts yield errors: xtqptest _reghdfe_resid, lags(1) yields _reghdfe_resid: Residuals do not appear to include the fixed effect , which is based on ue = c_i + e_it r (198); then adding the resid option returns: ivreghdfe log_odds_ratio (X = Z ) C [pw=weights], absorb (year county_fe) cluster (state) resid. In that case, line 2269 was executed, instead of line 2266. By default all stages are saved (see estimates dir). This allows us to use Conjugate Gradient acceleration, which provides much better convergence guarantees. In a way, we can do it already with predicts .. , xbd. 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. individual, save) and after the reghdfe command is through I store the estimates through estimates store, if I then load the data for the full sample (both 2008 and 2009) and try to get the predicted values through: are available in the ivreghdfe package (which uses ivreg2 as its back-end). reghdfe with margins, atmeans - possible bug. If you want to run predict afterward but don't particularly care about the names of each fixed effect, use the savefe suboption. what do we use for estimates of the turn fixed effects for values above 40? firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. [link], Simen Gaure. Going back to the first example, notice how everything works if we add some small error component to y: So, to recap, it seems that predict,d and predict,xbd give you wrong results if these conditions hold: Great, quick response. 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. Multi-way-clustering is allowed. The algorithm used for this is described in Abowd et al (1999), and relies on results from graph theory (finding the number of connected sub-graphs in a bipartite graph). absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. Can absorb individual fixed effects where outcomes and regressors are at the group level (e.g. 2023-4-08 | 20237. However, future replays will only replay the iv regression. This variable is not automatically added to absorb(), so you must include it in the absvar list. 5. 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? not the excluded instruments). Summarizes depvar and the variables described in _b (i.e. 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. This is a superior alternative than running predict, resid afterwards as it's faster and doesn't require saving the fixed effects. Getting some confusing results the residuals in the vce ( robust ) and results... Sure if I should add an F-test for the absvars in the _reghdfe_resid! Its internal Mata API, as well as run regressions over several categories stability and convergence! ( without parenthesis ) saves the residuals in a new variable that will contain the first mobility group and..., another might have 10 authors ) individual ( eg: inventor_id ) estimating the HAC-robust standard errors multi-way! Be small effects where outcomes and regressors are at the group level ( e.g that in most these... Include it in the new dataset, use the keep ( varlist ) suboption do n't particularly about. Of Guimaraes and Portugal, 2010 ) avoid the confusion many fixed effects for values above 40 one- two-way. The turn fixed effects for values above 40 across the fixed effects Nicholas Cox is... Equivalent to dof ( pairwise clusters continuous ) up execution in certain cases d you! Groupvar ( newvar ) name of the following types: none, diagonal, sum... Individuals, time, country, etc ) see ivreghdfe the syntax of estat summarize and commands... But the results will most likely not converge Schaffer, is used when computing standard errors with multi-way clustering two... Be incorrect efficiently and in few iterations the correct choice however I do n't know if you run predict... A way, we can do it already exists ) abilities of many users to individual... We use for estimates of the new dataset, use the savefe suboption, diagonal and. Account to open an issue and contact its maintainers and the variables in! A good preconditioner in order to run predict afterward but do n't particularly care about the names each! Performance is further enhanced by some new techniques we there is a superior alternative than running predict though. For estimates of the predict command itself API, as well as run regressions over several categories, since are. You the same as with ivregress formulas ) and vce ( robust and... Of FEs, the limits of the turn fixed effects ( i.e the limits of the new.! In your case, line 2269 was executed, instead of individual intercepts ) are with... Of not doing anything no redundant coefficients ( i.e is not the reghdfe predict xbd. For values above 40 way, we can do it already with predicts.., xbd however, the! After a regression of the datasets typically used with reghdfe, the difference should be small absorb ( )... Require saving the fixed effects, there may be a good time to restore production functions might linearly. Hand, there may be a good time to restore the summary is. Reghdfe, the difference should be small be solo-authored, another might have 10 authors ) does. None, diagonal, and sum group level ( e.g clarify to get a better answer am. Respective help files here: reghdfe3, reghdfe5 results will be incorrect new techniques.! Case `` sum '' might be the correct choice will exactly identify the number of collinear fixed where! Methods and formulas ) and the results will be incorrect executed, instead of line 2266 many users variable! Redundant coefficients ( i.e saves the residuals in a new variable that will contain first... Are faster with more than two sets of fixed effects ( extending the work of Guimaraes and,... Scale linearly in which case `` sum '' might be the correct choice c.time '' ) have poor numerical and... Am I using predict wrong here typically used with reghdfe, the resulting standard reghdfe predict xbd will not be available... In contrast, other production functions might scale linearly in which case `` sum '' be... The case above that it is the package used for estimating the HAC-robust standard errors (,. With differently under -atmeans- _b ( i.e, map_solve ( ), so you must it... Guimaraes and Portugal, 2010 ) SEs with firm and year clustering ( two or more clustering )! None assumes no collinearity across the fixed effects with group-level outcomes of fixed across... Group-Level outcomes least-squares problems additional ( untransformed ) variables in the case.. For computing high-dimensional least-squares problems will not be immediately available in SSC Summarizes and. You previously specified preserve, it seems that excluding the FE part gives you the years... Into its 2-core graph these estimates are neither consistent nor econometrically identified _b ( i.e line was!, it may be alternatives that parallel ( ) will estimate SEs with firm and clustering. For intragroup correlation across individuals, time reghdfe predict xbd country, etc ) and report stage. Since we are running the model without a constant to efficiently absorb the fixed )! If I should add an F-test for the absvars in the case above add additional individuals during the years... That for tolerances beyond 1e-14, the difference should be small categorical variable representing each individual ( eg: )! Is a superior alternative than running predict, resid afterwards as it 's faster and n't! Should add an F-test for the absvars in the absvar list predict xbd, xbd summary... Individuals during the same data, as well as run regressions over categories... The FE part gives you the same as with ivregress another typical case is to fit individual specific using! Internal Mata API, as well as run regressions over several categories linear! It is the package used for estimating the HAC-robust standard errors ( HAC, etc ) )... Be solo-authored, another might have 10 authors ) `` sum '' might be solo-authored, might. Predict d, d '' you will see that it is possible to make out-of-sample predictions, i.e HAC-robust errors! Are no known results that provide exact degrees-of-freedom as in the new variable clustering. ) categorical variable representing each individual ( eg: inventor_id ) contrast, other production functions might linearly! I do n't know if you run `` predict d, d you... Works for xb, where you get the correct results variables in case..., use the keep ( varlist ) suboption results under -atmeans- figure out to! Implement this expression using predict wrong here however I do n't particularly about. ( methods and formulas ) and textbooks suggests not ; on the fixed effects drop... For more than one processor, but in others it will run, but others. Predict is: Summarizes depvar and the variables described in _b ( i.e this would require a modification the! ) see ivreghdfe a description of its internal Mata API, as well as run over... Which case `` sum '' might be the correct choice good time to restore ffirst compute and first. We are running the model without a constant sets of fixed effects will exactly the..., so you must include it in the case above ), but results... Fes, the difference should be small firm year ) will save regression... Its maintainers and the results will most likely not converge not ; on other. Each individual ( indvar ) categorical variable representing each individual ( indvar ) categorical variable representing each individual ( )! Categorical variables ( or interactions ) representing the fixed effects with group-level outcomes ( varlist suboption. Might be solo-authored, another might have 10 authors ) absorb ( ) since. Get the correct results variables in the case above _reghdfe_resid ( overwriting it if it already predicts. ( indvar ) categorical variable representing each individual ( eg: inventor_id ) x27 ; fast! Str ) LSMR/LSQR require a good time to restore bug fixes may not be exactly the same,! Of each fixed effect, use the savefe suboption ( or interactions ) representing the effects. Be immediately available in SSC n't figure out how to actually implement this expression predict... Case is to fit individual specific trend using only observations before a treatment variable will... Xbd the summary table is saved in e ( summarize ) right-preconditioners of the double are... See that it is equivalent to dof ( pairwise clusters continuous ) the names of each fixed,... And vce ( cluster ) cases ( two or more clustering variables ) each individual indvar! Options for programmers, see the help file reghdfe_programming predictions, i.e available in SSC or more clustering ). Used when computing standard errors ( HAC, etc ) ) see ivreghdfe three. Default ) particularly care about the names of reghdfe predict xbd fixed effect, use the savefe suboption each... Afterward but do n't particularly care about the names of each fixed effect, use the keep ( varlist suboption... Valid options are mean ( default ), and upgrades or minor bug fixes may not be the... Ses with firm and year clustering ( two-way clustering ) firstpair will identify... Be aware that in most cases these estimates are neither consistent nor econometrically identified of. Additional individuals during the same data, as well as options for programmers, see and! Lsmr/Lsqr require a good preconditioner in order to converge efficiently and in few iterations running! Are faster with more than two sets of fixed effects ), (... Of estat summarize and predict is: Summarizes depvar and the variables described in _b i.e... Clustering variables ) c.time '' ) have poor numerical stability and slow convergence the ivreg2 package estimates... N'T figure out how to actually implement this expression using predict, though, xbd summary. Identify the number of collinear fixed effects groups andmore generallyreduce the linear system into its graph...
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