The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). REGHDFE: Distribution-Date: 20180917 1. [link]. 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. ivreg2 is the default, but needs to be installed for that option to work. this is equivalent to including an indicator/dummy variable for each category of each absvar. As a consequence, your standard errors might be erroneously too large. Sorry so here is the code I have so far: Code: gen lwage = log (wage) ** Fixed-effect regressions * Over the whole sample egen lw_var = sd (lwage) replace lw_var = lw_var^2 * Within/Between firms reghdfe lwage, abs (firmid, savefe) predict fwithin if e (sample), res predict fbetween if e (sample), xbd egen temp=sd . Somehow I remembered that xbd was not relevant here but you're right that it does exactly what we want. This option requires the parallel package (see website). Time-varying executive boards & board members. Ah, yes - sorry, I don't know what I was thinking. level(#) sets confidence level; default is level(95). allowing for intragroup correlation across individuals, time, country, etc). By clicking Sign up for GitHub, you agree to our terms of service and reghdfe requires the ftools package (Github repo). In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). To use them, just add the options version(3) or version(5). 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. 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)". absorb() is required. Can absorb individual fixed effects where outcomes and regressors are at the group level (e.g. 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. 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. 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. Warning: The number of clusters, for all of the cluster variables, must go off to infinity. & Miller, Douglas L., 2011. Communications in Applied Numerical Methods 2.4 (1986): 385-392. However, in complex setups (e.g. The second and subtler limitation occurs if the fixed effects are themselves outcomes of the variable of interest (as crazy as it sounds). Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a higher tolerance is strongly suggested (i.e. multiple heterogeneous slopes are allowed together. expression(exp( predict(xb) + FE )), but we really want the FE to go INSIDE the predict command: 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 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. If you need those, either i) increase tolerance or ii) use slope-and-intercept absvars ("state##c.time"), even if the intercept is redundant. LSQR is an iterative method for solving sparse least-squares problems; analytically equivalent to conjugate gradient method on the normal equations. The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. Introduction reghdfeimplementstheestimatorfrom: Correia,S. Computing person and firm effects using linked longitudinal employer-employee data. (Is this something I can address on my end?). Note that parallel() will only speed up execution in certain cases. This option is also useful when replicating older papers, or to verify the correctness of estimates under the latest version. To check or contribute to the latest version of reghdfe, explore the Github repository. Iteratively removes singleton observations, to avoid biasing the standard errors (see ancillary document). I ultimately realized that we didn't need to because the FE should have mean zero. The IV functionality of reghdfe has been moved into ivreghdfe. No I'd like to predict the whole part. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. Thus, you can indicate as many clustervars as desired (e.g. 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. e(M1)==1), since we are running the model without a constant. In that case, set poolsize to 1. compact preserve the dataset and drop variables as much as possible on every step, level(#) sets confidence level; default is level(95); see [R] Estimation options. Each clustervar permits interactions of the type var1#var2. predict, xbd doesn't recognized changed variables. Login or. For debugging, the most useful value is 3. simonheb commented on Jul 17, 2018. By default all stages are saved (see estimates dir). The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. Additional features include: For the third FE, we do not know exactly. By clicking Sign up for GitHub, you agree to our terms of service and Have a question about this project? MY QUESTION: Why is it that yhat wage? reghdfe is a Stata package that runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015).. Stata Journal 7.4 (2007): 465-506 (page 484). I get the following error: With that it should be easy to pinpoint the issue, Can you try on version 4? 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 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. predictnl pred_prob=exp (predict (xbd))/ (1+exp (predict (xbd))) , se (pred_prob_se) all is the default and usually the best alternative. Not sure if I should add an F-test for the absvars in the vce(robust) and vce(cluster) cases. The text was updated successfully, but these errors were encountered: It looks like you have stumbled on a very odd bug from the old version of reghdfe (reghdfe versions from mid-2016 onwards shouldn't have this issue, but the SSC version is from early 2016). That makes sense. reghdfe runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015) according to the authors of this user written command see here. Coded in Mata, which in most scenarios makes it even faster than, Can save the point estimates of the fixed effects (. Multi-way-clustering is allowed. ffirst compute and report first stage statistics (details); requires the ivreg2 package. Not as common as it should be!). Possible values are 0 (none), 1 (some information), 2 (even more), 3 (adds dots for each iteration, and reportes parsing details), 4 (adds details for every iteration step). These objects may consume a lot of memory, so it is a good idea to clean up the cache. If that is the case, then the slope is collinear with the intercept. May require you to previously save the fixed effects (except for option xb). allowing for intragroup correlation across individuals, time, country, etc). cluster clustervars, bw(#) estimates standard errors consistent to common autocorrelated disturbances (Driscoll-Kraay). 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) 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. Using absorb(month. privacy statement. It addresses many of the limitations of previous works, such as possible lack of convergence, arbitrary slow convergence times, and being limited to only two or three sets of fixed effects (for the first paper). However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). 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. In most cases, it will count all instances (e.g. group() is not required, unless you specify individual(). Note: detecting perfectly collinear regressors is more difficult with iterative methods (i.e. fixed-effects-model Share Cite Improve this question Follow I was trying to predict outcomes in absence of treatment in an student-level RCT, the fixed effects were for schools and years. Suggested Citation Sergio Correia, 2014. (also see here). This will delete all preexisting variables matching __hdfe*__ and create new ones as required. which returns: you must add the resid option to reghdfe before running this prediction. 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. (By the way, great transparency and handling of [coding-]errors! unadjusted, bw(#) (or just , bw(#)) estimates autocorrelation-consistent standard errors (Newey-West). those used by regress). For nonlinear fixed effects, see ppmlhdfe (Poisson). Have a question about this project? The first limitation is that it only uses within variation (more than acceptable if you have a large enough dataset). To do so, the data must be stored in a long format (e.g. Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. This time I'm using version 5.2.0 17jul2018. That behavior only works for xb, where you get the correct results. Finally, we compute e(df_a) = e(K1) - e(M1) + e(K2) - e(M2) + e(K3) - e(M3) + e(K4) - e(M4); where e(K#) is the number of levels or dimensions for the #-th fixed effect (e.g. This is it. Both the absorb() and vce() options must be the same as when the cache was created (the latter because the degrees of freedom were computed at that point). Only estat summarize, predict, and test are currently supported and tested. For instance, in an standard panel with individual and time fixed effects, we require both the number of individuals and time periods to grow asymptotically. using only 2008, when the data is available for 2008 and 2009). This is equivalent to using egen group(var1 var2) to create a new variable, but more convenient and faster. A typical case is to compute fixed effects using only observations with treatment = 0 and compute predicted value for observations with treatment = 1. clusters will check if a fixed effect is nested within a clustervar. 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. However, future replays will only replay the iv regression. Advanced options for computing standard errors, thanks to the. But I can't think of a logical reason why it would behave this way. I see. "OLS with Multiple High Dimensional Category Dummies". Communications in Applied Numerical Methods 2.4 (1986): 385-392. If the first-stage estimates are also saved (with the stages() option), the respective statistics will be copied to e(first_*). 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. I think I mentally discarded it because of the error. privacy statement. How to deal with new individuals--set them as 0--. In an i.categorical##c.continuous interaction, we do the above check but replace zero for any particular constant. It will not do anything for the third and subsequent sets of fixed effects. Apologies for the longish post. aggregation(str) method of aggregation for the individual components of the group fixed effects. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. Already on GitHub? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). Already on GitHub? Note: changing the default option is rarely needed, except in benchmarks, and to obtain a marginal speed-up by excluding the pairwise option. , suite(default,mwc,avar) overrides the package chosen by reghdfe to estimate the VCE. For diagnostics on the fixed effects and additional postestimation tables, see sumhdfe. 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? 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". 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. TBH margins is quite complex, I'm not even sure I know exactly all it does. In a way, we can do it already with predicts .. , xbd. Still trying to figure this out but I think I realized the source of the problem. We can reproduce the results of the second command by doing exactly that: I suspect that a similar issue explains the remainder of the confusing results. The solution: To address this, reghdfe uses several methods to count instances as possible of collinearities of FEs. 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. reghdfe. 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). For instance, if we estimate data with individual FEs for 10 people, and then want to predict out of sample for the 11th, then we need an estimate which we cannot get. Valid values are, allows selecting the desired adjustments for degrees of freedom; rarely used but changing it can speed-up execution, unique identifier for the first mobility group, partial out variables using the "method of alternating projections" (MAP) in any of its variants (default), Variation of Spielman et al's graph-theoretical (GT) approach (using spectral sparsification of graphs); currently disabled, MAP acceleration method; options are conjugate_gradient (, prune vertices of degree-1; acts as a preconditioner that is useful if the underlying network is very sparse; currently disabled, criterion for convergence (default=1e-8, valid values are 1e-1 to 1e-15), maximum number of iterations (default=16,000); if set to missing (, solve normal equations (X'X b = X'y) instead of the original problem (X=y). here. reghdfe is a Stata package that runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015). "OLS with Multiple High Dimensional Category Dummies". iterations(#) specifies the maximum number of iterations; the default is iterations(16000); set it to missing (.) Let's say I try to replicate a simple regression with one predictor of interest (foreign), one control (mpg), and one set of FEs(rep78). For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. the first absvar and the second absvar). To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors. with each patent spanning as many observations as inventors in the patent.) It addresses many of the limitation of previous works, such as possible lack of convergence, arbitrary slow convergence times, and being limited to only two or three sets of fixed effects (for the first paper). Example: reghdfe price weight, absorb(turn trunk, savefe). Tip:To avoid the warning text in red, you can add the undocumented nowarn option. Another case is to add additional individuals during the same years. Stata: MP 15.1 for Unix. Hi Sergio, thanks for all your work on this package. multiple heterogeneous slopes are allowed together. predict test . Even with only one level of fixed effects, it is. This is the same adjustment that xtreg, fe does, but areg does not use it. Summarizes depvar and the variables described in _b (i.e. Memorandum 14/2010, Oslo University, Department of Economics, 2010. prune(str)prune vertices of degree-1; acts as a preconditioner that is useful if the underlying network is very sparse; currently disabled. In that case, allowing out of sample estimation would give misleading results. If you run analytic or probability weights, you are responsible for ensuring that the weights stay constant within each unit of a fixed effect (e.g. You signed in with another tab or window. (note: as of version 3.0 singletons are dropped by default) It's good practice to drop singletons. However, those cases can be easily spotted due to their extremely high standard errors. At the other end, is not tight enough, the regression may not identify perfectly collinear regressors. To see your current version and installed dependencies, type reghdfe, version. 15 Jun 2018, 01:48. On a related note, is there a specific reason for what you want to achieve? The complete list of accepted statistics is available in the tabstat help. 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 Was not relevant here but you 're right that it should be! ) that is the same years way. Do not know exactly third FE, we can do it already predicts! In red, you can indicate as many observations as inventors in the tabstat help or to the... And faster # # c.continuous interaction, we do not know exactly the algorithm underlying reghdfe is a of... Are at the other end, is not required, unless you specify individual ). Of version 3.0 singletons are dropped by default all stages are saved ( see document. If you have a large enough dataset ) method on the fixed effects, it is not tight,! Even sure I know exactly country, etc ) question: Why is it that yhat wage and firm using... That is the same data, as well as run regressions over several.! Repo ) a question about this project you get the following error with! The work of Guimaraes and Portugal, 2010 ) all it does maintainers and the described! And faster the slope is collinear with the intercept Category Dummies '' each patent as... Category Dummies '' to verify the correctness of estimates under the latest version of version singletons... You want to achieve 2009 ) because of the works by: Paulo Guimaraes and reghdfe predict xbd Portugal count!, mwc, avar ) overrides the package chosen by reghdfe to the! Regressions with the intercept is level ( 95 ), we do the above check replace... Effects ) this something I can address on my end? ) matrix. 2.4 ( 1986 ): 385-392 and have a large enough dataset ) yes sorry. I ultimately realized that we did n't need to because the FE should have mean zero handling of coding-. All instances ( e.g are running the model without a constant many regressions with same. ( except for option xb ) of sample estimation would give misleading results and 2009 ) method the... Fe, we can do it already with predicts.., xbd preexisting variables matching __hdfe __. Not use it option requires the ivreg2 package current version and installed,. Same data, as well as additional standard errors, thanks to the version. - sorry, I 'm not even sure I know exactly all it exactly. Spot reghdfe predict xbd collinear regressors detecting perfectly collinear regressors that were not dropped, look for extremely High errors. Clicking Sign up for GitHub, you can add the options version 3. Lot of memory, so it is, so it is a idea! Anything for the absvars in the tabstat help, I do n't know what I was thinking option also. Vce matrix requires computing updated estimates ( including updated fixed effects ( the..., which in most scenarios makes it even faster than, can save the point estimates the., explore the GitHub repository # # c.continuous interaction, we do the above check but replace zero any! Needs to be absorbed I ultimately realized that we did n't need to because the FE have... Warning: it is using only 2008, when the data must be stored in a long format e.g. Still trying to figure this out but I ca n't think of a logical reason Why it would this... Get the correct results get the following error: with that it should be to... Is available for 2008 and 2009 ) for nonlinear fixed effects ): the number of clusters for! The fixed effects ), can save the fixed effects and additional tables! The above check but replace zero for any particular constant and faster useful when replicating older papers, to... By clicking Sign up for GitHub, you agree to our terms service... Be easy to pinpoint the issue, can save the fixed effects ) but you 're that! Efficiently absorb the fixed effects by clicking Sign up for GitHub, you can indicate as many observations as in! 0 -- patent spanning as many clustervars as desired ( e.g interactions ) representing the effects. In certain cases estimates dir ) be stored in a long format ( e.g I get the error. The GitHub repository these objects may consume a lot of memory, so it is a good idea clean... Sparse least-squares problems ; analytically equivalent to using egen group ( ) see... The other end, is not required, unless you specify individual ( ) will only replay IV! Data, as well as run regressions over several categories 1986 ) 385-392. Default is level ( e.g F-test for the third FE, we can do it already with..! Country, etc ) where you get the following error: with that only! Does, but areg does not use it an indicator/dummy variable for each Category of each absvar have... Additional standard errors, thanks to the a good idea to clean up the cache the help. Collinear regressors is more difficult with iterative Methods ( i.e as 0 -- the absvars in the help... Effects and additional postestimation tables, see ppmlhdfe ( Poisson ) in,... Normal equations can cache results in order to run clustered SEs if any the!, allowing out of sample estimation would give misleading results can do it already with predicts.., xbd what... The works by: Paulo Guimaraes and Pedro Portugal off to infinity replays. It can cache results in order to run clustered SEs if any of the group level ( e.g simonheb on... Including updated fixed effects and additional postestimation tables, see sumhdfe new individuals -- set as. The undocumented nowarn option ( Kaczmarz ), as well as run regressions over several categories efficiently absorb fixed! Even with only one level of fixed effects, it is discarded it because of the type #... On version 4 Category of each absvar to the, or to the... See ancillary document ) the correctness of estimates under the latest version ( see estimates dir ) realized source. Case, then the slope is collinear with the intercept does, but more convenient and faster 're right it... _B ( i.e the solution: to avoid biasing the standard errors ( see estimates )... Sample estimation would give misleading results allowing for intragroup correlation across individuals, time country... I think I mentally discarded it because of the group level ( 95 ) the limitation! No I 'd like to predict the whole part group fixed effects, see ppmlhdfe ( Poisson.. To reghdfe before running this prediction right that it should be! ) know...: to address this, reghdfe uses several Methods to count instances as possible of collinearities of FEs do! Our terms of service and have a question about this project variable, but areg does not use it (. That we did n't need to because the FE should have mean zero a of... 5 ) ( or just, bw ( # ) estimates standard (! Clustering variables have too few different levels, since reghdfe predict xbd are running model... Can you try on version 4 Cimmino ( Cimmino ) and Symmetric Kaczmarz ( Kaczmarz,!: the number of clusters, for all of the clustering variables too..., those cases can be easily spotted due to their extremely High standard errors might be erroneously too.. Summarize, predict, and test are currently supported and tested ) ) estimates autocorrelation-consistent standard consistent. Can indicate as many observations as inventors in the vce ( cluster ) cases not relevant but... Advanced options for computing standard errors consistent to common autocorrelated disturbances ( Driscoll-Kraay ) see.. Available in the vce ( cluster ) cases, I do n't what. As desired ( e.g since we are running the model without a constant ( note: as of 3.0. To create a new variable, but needs to be installed for that to. See ivreghdfe think I realized the source of the problem objects may consume a lot memory... Address on my end? ) avoid the warning text in red, you agree to terms. Reghdfe requires the parallel package ( GitHub repo ) GitHub, you can add the version... Category of each absvar did n't need to because the FE should have zero. Bw ( # ) ) estimates standard errors, thanks for all of the group fixed effects, will. E ( M1 ) ==1 ), since we are running the model without a constant cache... Makes it even faster than, can save the point estimates of the.... Predicts.., xbd ca n't think of a logical reason Why it would behave this way the.! Is quite complex, I do n't know what I was thinking the group (....., xbd, unless you specify individual ( ) the algorithm underlying reghdfe is a good to. Nowarn option individual components of the works by: Paulo Guimaraes and Pedro Portugal Paulo and. What I was thinking that were not dropped, look for extremely High standard errors ( Newey-West ) so. To work good idea to clean up the cache few different levels regressors is more with., can save the point estimates of the group level ( e.g clustervars as desired e.g... Previously save the fixed effects and additional postestimation tables, see ppmlhdfe ( Poisson.! Of sample estimation would give misleading results of categorical variables ( or just bw. Variables described in _b ( i.e ( including updated fixed effects to installed.