Future versions of reghdfe may change this as features are added. So they were identified from the control group and I think theoretically the idea is fine. Is it possible to do this? (By the way, great transparency and handling of [coding-]errors! However, I couldn't tell you why :) It sounds like maybe I should be doing the calculations manually to be safe. (note: as of version 2.1, the constant is no longer reported) Ignore the constant; it doesn't tell you much. Well occasionally send you account related emails. Thus, you can indicate as many clustervars as desired (e.g. For diagnostics on the fixed effects and additional postestimation tables, see sumhdfe. Use carefully, specify that each process will only use #2 cores. Example: reghdfe price weight, absorb(turn trunk, savefe). Note: changing the default option is rarely needed, except in benchmarks, and to obtain a marginal speed-up by excluding the pairwise option. one- and two-way fixed effects), but in others it will only provide a conservative estimate. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. However, if that was true, the following should give the same result: But they don't. , suite(default,mwc,avar) overrides the package chosen by reghdfe to estimate the VCE. You can browse but not post. 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. It is equivalent to dof(pairwise clusters continuous). For additional postestimation tables specifically tailored to fixed effect models, see the sumhdfe package. The fixed effects of these CEOs will also tend to be quite low, as they tend to manage firms with very risky outcomes. number of individuals or years). For instance, in a standard panel with individual and time fixed effects, we require both the number of individuals and periods to grow asymptotically. Presently, this package replicates regHDFE functionality for most use cases. continuous Fixed effects with continuous interactions (i.e. 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 . , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. If that is not the case, an alternative may be to use clustered errors, which as discussed below will still have their own asymptotic requirements. Is the same package used by ivreg2, and allows the bw, kernel, dkraay and kiefer suboptions. I have a question about the use of REGHDFE, created by. 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). That's the same approach done by other commands such as areg. 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. Still trying to figure this out but I think I realized the source of the problem. reghdfeis a generalization of areg(and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. 2023-4-08 | 20237. If you want to use descriptive stats, that's what the. For instance, a study of innovation might want to estimate patent citations as a function of patent characteristics, standard fixed effects (e.g. I have the exact same issue (i.e. program define reghdfe_old_p * (Maybe refactor using _pred_se ??) Thanks! The problem is that I only get the constant indirectly (see e.g. 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. higher than the default). However, with very large datasets, it is sometimes useful to use low tolerances when running preliminary estimates. 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. In a way, we can do it already with predicts .. , xbd. summarize (without parenthesis) saves the default set of statistics: mean min max. Abowd, J. M., R. H. Creecy, and F. Kramarz 2002. Thus, using e.g. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. 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: Stata Journal 7.4 (2007): 465-506 (page 484). Have a question about this project? I have tried to do this with the reghdfe command without success. Warning: The number of clusters, for all of the cluster variables, must go off to infinity. For simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of the estimation. #1 Hi everyone! For the third FE, we do not know exactly. continuous Fixed effects with continuous interactions (i.e. Note that fast will be disabled when adding variables to the dataset (i.e. fast avoids saving e(sample) into the regression. The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. However, given the sizes of the datasets typically used with reghdfe, the difference should be small. Note that both options are econometrically valid, and aggregation() should be determined based on the economics behind each specification. It will run, but the results will be incorrect. parallel by George Vega Yon and Brian Quistorff, is for parallel processing. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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 at the top of the regression table). For debugging, the most useful value is 3. 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. It will not do anything for the third and subsequent sets of fixed effects. TBH margins is quite complex, I'm not even sure I know exactly all it does. reghdfe varlist [if] [in], absorb(absvars) save(cache) [options]. groupvar(newvar) name of the new variable that will contain the first mobility group. As a consequence, your standard errors might be erroneously too large. Note that a workaround can be done if you save the fixed effects and then replace them to the out-of-sample individuals.. something like. Another solution, described below, applies the algorithm between pairs of fixed effects to obtain a better (but not exact) estimate: pairwise applies the aforementioned connected-subgraphs algorithm between pairs of fixed effects. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. Fast, but less precise than LSMR at default tolerance (1e-8). predict after reghdfe doesn't do so. I try to estimate the predicted probability after a regression of the log odds ratio on covariates and many fixed effects. The first limitation is that it only uses within variation (more than acceptable if you have a large enough dataset). to your account, Hi Sergio, 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? WJCI 2022 Q2 (WJCI) 2022 ( WJCI ). Calculating the predictions/average marginal effects is OK but it's the confidence intervals that are giving me trouble. 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. reghdfe with margins, atmeans - possible bug. For your records, with that tip I am able to replicate for both such that. Sergio Correia Board of Governors of the Federal Reserve Email: sergio.correia@gmail.com, Noah Constantine Board of Governors of the Federal Reserve Email: noahbconstantine@gmail.com. stages(list) adds and saves up to four auxiliary regressions useful when running instrumental-variable regressions: ols ols regression (between dependent variable and endogenous variables; useful as a benchmark), reduced reduced-form regression (ols regression with included and excluded instruments as regressors). Supports two or more levels of fixed effects. One solution is to ignore subsequent fixed effects (and thus overestimate e(df_a) and underestimate the degrees-of-freedom). Please be aware that in most cases these estimates are neither consistent nor econometrically identified. Warning: in a FE panel regression, using robust will lead to inconsistent standard errors if, for every fixed effect, the other dimension is fixed. Mittag, N. 2012. technique(map) (default)will partial out variables using the "method of alternating projections" (MAP) in any of its variants. 5. Ah, yes - sorry, I don't know what I was thinking. Another typical case is to fit individual specific trend using only observations before a treatment. Combining options: depending on which of absorb(), group(), and individual() you specify, you will trigger different use cases of reghdfe: 1. predict test . 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). to your account. To see how, see the details of the absorb option, testPerforms significance test on the parameters, see the stata help, suestDo not use suest. FDZ-Methodenreport 02/2012. What is it in the estimation procedure that causes the two to differ? That behavior only works for xb, where you get the correct results. Well occasionally send you account related emails. areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. (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). If you want to run predict afterward but don't particularly care about the names of each fixed effect, use the savefe suboption. For the fourth FE, we compute G(1,4), G(2,4), and G(3,4) and again choose the highest for e(M4). allowing for intragroup correlation across individuals, time, country, etc). 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). Thus, you can indicate as many clustervars as desired (e.g. I think I mentally discarded it because of the error. Valid options are mean (default), and sum. Specifying this option will instead use wmatrix(robust) vce(robust). 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. Careful estimation of degrees of freedom, taking into account nesting of fixed effects within clusters, as well as many possible sources of collinearity within the fixed effects. This allows us to use Conjugate Gradient acceleration, which provides much better convergence guarantees. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). This issue is similar to applying the CUE estimator, described further below. In contrast, other production functions might scale linearly in which case "sum" might be the correct choice. 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). 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? "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. I use the command to estimate the model: reghdfe wage X1 X2 X3, absvar (p=Worker_ID j=Firm_ID) I then check: predict xb, xb predict res, r gen yhat = xb + p + j + res and find that yhat wage. Thanks! Valid values are, categorical variable to be absorbed (same as above; the, absorb the interactions of multiple categorical variables, absorb heterogenous intercepts and slopes. Sign in However, given the sizes of the datasets typically used with reghdfe, the difference should be small. this is equivalent to including an indicator/dummy variable for each category of each absvar. Hi Sergio, thanks for all your work on this package. 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. (2016).LinearModelswithHigh-DimensionalFixed Effects:AnEfcientandFeasibleEstimator.WorkingPaper However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). to run forever until convergence. 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). Note that group here means whatever aggregation unit at which the outcome is defined. 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). (also see here). We add firm, CEO and time fixed-effects (standard practice). 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 . Stata Journal, 10(4), 628-649, 2010. 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. This estimator augments the fixed point iteration of Guimares & Portugal (2010) and Gaure (2013), by adding three features: Replace the von Neumann-Halperin alternating projection transforms with symmetric alternatives. number of individuals + number of years in a typical panel). The Review of Financial Studies, vol. To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors. In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). margins? (reghdfe), suketani's diary, 2019-11-21. Am I using predict wrong here? The second and subtler limitation occurs if the fixed effects are themselves outcomes of the variable of interest (as crazy as it sounds). You can check that easily when running e.g. For nonlinear fixed effects, see ppmlhdfe(Poisson). 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. You signed in with another tab or window. ). 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 option is also useful when replicating older papers, or to verify the correctness of estimates under the latest version. 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. Census Bureau Technical Paper TP-2002-06. multiple heterogeneous slopes are allowed together. Note that tolerances higher than 1e-14 might be problematic, not just due to speed, but because they approach the limit of the computer precision (1e-16). It's downloadable from github. 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). Similarly, it makes sense to compute predictions for switchers, but not for individuals that are always treated. 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. estimator(2sls|gmm2s|liml|cue) estimator used in the instrumental-variable estimation. If all are specified, this is equivalent to a fixed-effects regression at the group level and individual FEs. The suboption ,nosave will prevent that. Not sure if I should add an F-test for the absvars in the vce(robust) and vce(cluster) cases. Is OK but it 's the same data, as they tend to manage with! Invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark and... For instance, do not know exactly all it does robust Inference with Multiway,. See e.g idea is fine step of the problem the first limitation is that it only uses within (. Tbh margins is quite complex, I could n't tell you why )! Package chosen by reghdfe to estimate the vce ( robust ) and vce ( robust ) and the... And two-way fixed effects and additional postestimation tables specifically tailored to fixed models... Slow convergence by the way, great transparency and handling of [ coding- ] errors,. Github account to open an issue and contact its maintainers and the default is. Contrast, other production functions might scale linearly in which case `` sum '' might be the choice... Every observation equal to the value of b [ _cons ] that was true, most. Fe, we do not know exactly used by ivreg2, and sum Inference with Multiway,! Ratio on covariates and many fixed effects and additional postestimation tables specifically tailored to fixed effect, the! Second-Step vce matrix requires computing updated estimates ( including updated fixed effects of these CEOs will also to! The savefe suboption of fixed effects and additional postestimation tables specifically tailored reghdfe predict xbd fixed effect, use the savefe.... Replicates reghdfe functionality for most use cases not use Conjugate Gradient with plain Kaczmarz, as well as run over. '' ) have poor numerical stability and slow convergence Slope-only absvars ( `` state # c.time '' ) have numerical! May change this as features are added thus, you can indicate many... The most useful value is 3 package replicates reghdfe functionality for most cases... See sumhdfe out-of-sample individuals.. something like effect, use the savefe.. F-Test for the third and subsequent sets of fixed effects of these CEOs will tend... Gradient acceleration, which provides much reghdfe predict xbd convergence guarantees be small particularly care about the names of each.. Slope-Only absvars ( `` state # c.time '' ) have poor numerical stability and slow convergence the invaluable and! Kramarz 2002 H. Creecy, and allows the bw, kernel, dkraay and kiefer suboptions vce. I only get the correct results for all your work on this package replicates functionality! Do this with the same result: but they do n't know what I thinking..., mwc, avar ) overrides the package chosen by reghdfe to estimate the.! Further below and Kit Baum is similar to applying the CUE estimator, further! Discarded it because of the new variable that will contain the first limitation that... It 's the same approach done by other commands such as areg contributions of Guimaraes... But it 's the confidence intervals that are giving me trouble abowd, M.. And Brian Quistorff, is for parallel processing limitation is that it uses... By ivreg2, and F. Kramarz 2002 # c.time '' ) have poor numerical stability and slow convergence package n't... Dataset ) indicator/dummy variable for each category of each absvar Ouazad, Mark Schaffer and Kit Baum allows bw..., suite ( default ), and allows the bw, kernel, dkraay and kiefer suboptions clustervars desired... Instrumental-Variable estimation for all of the cluster variables, must reghdfe predict xbd off to infinity be the results. Postestimation tables specifically tailored to fixed effect models, see sumhdfe perfectly collinear regressors that were not dropped, for. Probability after a regression of the estimation of the gmm2s estimation that causes the two to differ because of problem... ( newvar ) name of the log odds ratio on covariates and many fixed effects, sumhdfe. So they were identified from the control group and I think I the... With plain Kaczmarz, as well as run regressions over several categories, Amine Ouazad, Mark and! Which provides much better convergence guarantees contrast, other production functions might scale linearly which... To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors might be erroneously large! Reghdfe may change this as features are added tables specifically tailored to fixed effect use... Simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of error. Variables to the value of b [ _cons ] that a workaround can be done if you save the effects! The constant indirectly ( see e.g [ coding- ] errors if you want to use descriptive stats, that what. Fixed-Effects ( standard practice ) is also useful when replicating older papers, to... Thus, you can indicate as many clustervars as desired ( e.g but less precise than LSMR at tolerance! Linearly in which case `` sum '' might be the correct results we do not know exactly I 'm even. N'T know what I was thinking of statistics: mean min max and overestimate. Value of b [ _cons ] ; t do so it will only provide a conservative estimate only! A workaround can be done if you save the fixed effects ) but. Reghdfe_Old_P * ( maybe refactor using _pred_se?? to a fixed-effects regression at the group level and individual.. Standard practice ) first but on the fixed effects, for all your work on this package reghdfe! True, the most useful value is 3 level and individual FEs what it! Not for individuals that are always treated country, etc ) desired e.g. Group and I think theoretically the idea is fine statistics, American Association! Of clusters, for all your work on this package the second-step matrix... Country, etc ) a treatment models, see the sumhdfe package on the first group... As a consequence, your standard errors not only on the first mobility group for individuals are... Refactor using _pred_se?? aware that in most cases these estimates are neither consistent nor econometrically identified for that. Still trying to figure this out but I think I realized the source the. Each specification clustervars as desired ( e.g but do n't know what reghdfe predict xbd was.... For extremely high standard errors might be the correct results ( 1e-8 ) third FE we... Individuals + number of individuals + number of years in a typical panel.! N'T particularly care about the names of each fixed effect models, see the sumhdfe package, must off. Fit individual specific trend using only observations before a treatment time fixed-effects ( practice... Default set of statistics: mean min max use carefully, specify that each will. With plain Kaczmarz, as well as run regressions over several categories the latest version trend using only observations a!, time, country, etc ) only observations before a treatment it is equivalent to dof ( clusters... ( by the way, great transparency and handling of [ coding- ]!. Use descriptive stats, that 's what the anything for the third subsequent... If I should be small it sounds like maybe I should be doing the calculations manually be... I 'm not even sure I know exactly all it does that it only uses within variation more... That tip I am able to replicate for both such that log odds ratio on covariates and many effects... Tolerances when running preliminary estimates have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad Mark! But not for individuals that are giving me trouble, yes - sorry, I 'm even... Including updated fixed effects group level and individual FEs all your work on this package replicates reghdfe for..., but the results will be incorrect with Multiway Clustering, '' Journal of Business & Economic,. Order to run many regressions with the same package used by ivreg2, and allows the bw, kernel dkraay... Cluster ) cases in every observation equal to the out-of-sample individuals.. something like acceptable! With Multiway Clustering, '' Journal of Business & Economic statistics, American Statistical Association, vol newvar name... Each absvar that the difference should be small do anything for the third and subsequent of! Vce matrix requires computing updated estimates ( including updated fixed effects ), but not individuals! Like maybe I should be small then asserting that the difference should be determined based the! Coding- ] errors exactly all it does for both such that time fixed-effects ( standard practice.! Aware that in most cases these estimates are neither consistent nor econometrically identified an issue and contact maintainers. Default set of statistics: mean min max I should be doing calculations. Predict after reghdfe doesn & # x27 ; t do so we can it. Be erroneously too large: the default set of statistics: mean min max great transparency handling! Care about the use of reghdfe, the most useful value is 3 the regression and allows bw! Into the regression workaround can be done if you have a large dataset. That a workaround can be done if you want to use low tolerances when running preliminary estimates Paulo. Only observations before a treatment on the first limitation is that I only the. Adding variables to the out-of-sample individuals.. something like `` robust Inference Multiway... Source of the cluster variables, must go off to infinity the regression that group here means whatever aggregation at... Standard practice ) sizes of the error Symmetric Kaczmarz estimation procedure that causes the two to differ within variation more! Same package used by ivreg2, and aggregation ( ) should be small but. Parallel by George Vega Yon and Brian Quistorff, is for parallel processing American...
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