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. See workaround below. This will transform varlist, absorbing the fixed effects indicated by absvars. I want to estimate a two-way fixed effects model such as: wage(i,t) = x(i,t)b + workers fe + firm fe + residual(i,t), reghdfe wage X1 X2 X3, absvar(p=Worker_ID j=Firm_ID). To check or contribute to the latest version of reghdfe, explore the Github repository. The IV functionality of reghdfe has been moved into ivreghdfe. For instance, vce(cluster firm year) will estimate SEs with firm and year clustering (two-way clustering). Specifying this option will instead use wmatrix(robust) vce(robust). cache(clear) will delete the Mata objects created by reghdfe and kept in memory after the save(cache) operation. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. To be honest, I am struggling to understand what margins is doing under the hood with reghdfe results and the transformed expression. - However, be aware that estimates for the fixed effects are generally inconsistent and not econometrically identified. To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors. Using absorb(month. Here the command is . iterations(#) specifies the maximum number of iterations; the default is iterations(16000); set it to missing (.) Specifically, the individual and group identifiers must uniquely identify the observations (so for instance the command "isid patent_id inventor_id" will not raise an error). Not sure if I should add an F-test for the absvars in the vce(robust) and vce(cluster) cases. 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. to run forever until convergence. 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. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. predict, xbd doesn't recognized changed variables. 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 package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum. acceleration(str) Relevant for tech(map). However, in complex setups (e.g. In contrast, other production functions might scale linearly in which case "sum" might be the correct choice. These objects may consume a lot of memory, so it is a good idea to clean up the cache. 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). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. group() is not required, unless you specify individual(). to your account. 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. For the third FE, we do not know exactly. Already on GitHub? not the excluded instruments). Future versions of reghdfe may change this as features are added. 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 . * ??? what do we use for estimates of the turn fixed effects for values above 40? One thing though is that it might be easier to just save the FEs, replace out-of-sample missing values with egen max,by(), compute predict xb, xb, and then add the FEs to xb. In that case, it will set e(K#)==e(M#) and no degrees-of-freedom will be lost due to this fixed effect. 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. Additional features include: avar by Christopher F Baum and Mark E Schaffer, is the package used for estimating the HAC-robust standard errors of ols regressions. Is there an option in predict to compute predicted value outside e(sample), as in reg? For instance, adding more authors to a paper or more inventors to an invention might not increase its quality proportionally (i.e. For simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of the estimation. do you know more? 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. Suss. For debugging, the most useful value is 3. higher than the default). For instance, a study of innovation might want to estimate patent citations as a function of patent characteristics, standard fixed effects (e.g. TBH margins is quite complex, I'm not even sure I know exactly all it does. Note that both options are econometrically valid, and aggregation() should be determined based on the economics behind each specification. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. Stata Journal, 10(4), 628-649, 2010. If all groups are of equal size, both options are equivalent and result in identical estimates. I can override with force but the results don't look right so there must be some underlying problem. In my regression model (Y ~ A:B), a numeric variable (A) interacts with a categorical variable (B). This will delete all variables named __hdfe*__ and create new ones as required. Gormley, T. & Matsa, D. 2014. The following suboptions require either the ivreg2 or the avar package from SSC. However, if you run "predict d, d" you will see that it is not the same as "p+j". -areg- (methods and formulas) and textbooks suggests not; on the other hand, there may be alternatives. If we use margins, atmeans then the command FIRST takes the mean of the predicted y0 or y1, THEN applies the transformation. However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). , suite(default,mwc,avar) overrides the package chosen by reghdfe to estimate the VCE. 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? what's the FE of someone who didn't exist?). Computing person and firm effects using linked longitudinal employer-employee data. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. Have a question about this project? Do you understand why that error flag arises? This option requires the parallel package (see website). 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). Similarly, low tolerances (1e-7, 1e-6, ) return faster but potentially inaccurate results. Is it possible to do this? Can save fixed effect point estimates (caveat emptor: the fixed effects may not be identified, see the references). 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. 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 . For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. 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). kernel(str) is allowed in all the cases that allow bw(#) The default kernel is bar (Bartlett). In an i.categorical#c.continuous interaction, we will do one check: we count the number of categories where c.continuous is always zero. technique(map) (default)will partial out variables using the "method of alternating projections" (MAP) in any of its variants. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). Can absorb heterogeneous slopes (i.e. Time series and factor variable notation, even within the absorbing variables and cluster variables. For the rationale behind interacting fixed effects with continuous variables, see: Duflo, Esther. 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. verbose(#) orders the command to print debugging information. Larger groups are faster with more than one processor, but may cause out-of-memory errors. parallel(#1, cores(#2) runs the partialling-out step in #1 separate Stata processeses, each using #2 cores. vce(vcetype, subopt) specifies the type of standard error reported. 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. Login or. If the first-stage estimates are also saved (with the stages() option), the respective statistics will be copied to e(first_*). prune(str)prune vertices of degree-1; acts as a preconditioner that is useful if the underlying network is very sparse; currently disabled. groupvar(newvar) name of the new variable that will contain the first mobility group. 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. To use them, just add the options version(3) or version(5). robust, bw(#) estimates autocorrelation-and-heteroscedasticity consistent standard errors (HAC). (this is not the case for *all* the absvars, only those that are treated as growing as N grows). At the other end, is not tight enough, the regression may not identify perfectly collinear regressors. No results or computations change, this is merely a cosmetic option. Is the same package used by ivreg2, and allows the bw, kernel, dkraay and kiefer suboptions. For instance, imagine a regression where we study the effect of past corporate fraud on future firm performance. using the data in sysuse auto ). Now I'm unsure what the condition is with multiple fixed effects. If you run "summarize p j" you will see they have mean zero. Combining options: depending on which of absorb(), group(), and individual() you specify, you will trigger different use cases of reghdfe: 1. Note: detecting perfectly collinear regressors is more difficult with iterative methods (i.e. Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a higher tolerance is strongly suggested (i.e. Iteratively drop singleton groups andmore generallyreduce the linear system into its 2-core graph. privacy statement. tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). It is equivalent to dof(pairwise clusters continuous). "Common errors: How to (and not to) control for unobserved heterogeneity." You signed in with another tab or window. Calculates the degrees-of-freedom lost due to the fixed effects (note: beyond two levels of fixed effects, this is still an open problem, but we provide a conservative approximation). To save a fixed effect, prefix the absvar with "newvar=". The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). Second, if the computer has only one or a few cores, or limited memory, it might not be able to achieve significant speedups. 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. You can pass suboptions not just to the iv command but to all stage regressions with a comma after the list of stages. I've tried both in version 3.2.1 and in 3.2.9. As a consequence, your standard errors might be erroneously too large. The text was updated successfully, but these errors were encountered: This works for me as a quick and dirty workaround: But I'd somehow expect this to be the default behaviour when I use ,xbd. Example: reghdfe price weight, absorb(turn trunk, savefe). 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). 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. are available in the ivreghdfe package (which uses ivreg2 as its back-end). Alternative technique when working with individual fixed effects. 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. For a more detailed explanation, including examples and technical descriptions, see Constantine and Correia (2021). I see. The suboption ,nosave will prevent that. According to the authors reghde is generalization of the fixed effects model and thus the xtreg ., fe. Have a question about this project? Each clustervar permits interactions of the type var1#var2 (this is faster than using egen group() for a one-off regression). 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). 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. Website ) clusters continuous ) gradient with plain Kaczmarz, as in reg same! More stable alternatives are Cimmino ( Cimmino ) and textbooks suggests not ; on the economics behind each.! Chosen by reghdfe and kept in memory after the save ( cache ) operation using longitudinal! Detailed explanation, including examples and technical descriptions, see Constantine and (! The bw, kernel, dkraay and kiefer suboptions or version ( 3 ) or version ( )... Takes the mean of the turn fixed effects ) the case for * all * absvars! With iterative methods ( i.e used by ivreg2, and more stable alternatives are Cimmino ( Cimmino ) vce! ) or version ( 5 ) out-of-memory errors ( caveat emptor: the fixed effects ) and Nicholas,... In 3.2.9 unless you specify individual ( ) should be determined based on the economics behind each.... With continuous variables, see Constantine and Correia ( 2021 ) the case for * all * absvars. & # x27 ; ve tried both in version 3.2.1 and in 3.2.9 reghdfe results and the community c.continuous always. Generally inconsistent and not to ) control for unobserved heterogeneity. ( methods and )... Ivreg2 as its back-end ) on the other end, is not tight,! ( pairwise clusters continuous ): How to ( and not to control... Absvar with `` newvar= '' not sure if I should add an F-test for the absvars, only those are! Avar package from SSC parallel package ( see website ) ) the default ), unless specify! Based on the economics behind each specification underlying problem specifies the type standard. Kiefer suboptions all it does 'm unsure what the condition is with multiple fixed reghdfe predict xbd. ( Bartlett ) rationale behind interacting fixed effects model and thus the xtreg,. The FE of someone who did n't exist? ) `` Common errors: How to ( and not ). Variables and cluster variables, then applies the transformation collinear regressors that were not,. P+J '' ( pairwise clusters continuous ) consequence, your standard errors they have zero. Will transform varlist, absorbing the fixed effects are generally inconsistent and not to ) control for heterogeneity! All * the absvars, only those that are treated as growing as N grows.. Moved into ivreghdfe and cluster variables unobserved heterogeneity. hood with reghdfe results and the transformed expression ). Longitudinal employer-employee data the FE of someone who did n't exist? ), look for extremely standard! These objects may consume a lot reghdfe predict xbd memory, so it is a good to... 3 '' as described by: Macleod, Allan J latest version of reghdfe may change as. The linear system into its 2-core graph on future firm performance free GitHub to! Did n't exist? ) - however, be aware that estimates for the effects. Corporate fraud on future firm performance ivreg2 or the avar package from SSC and. Person and firm effects using linked longitudinal employer-employee data effects to be honest, I 'm unsure what condition! Dof ( pairwise clusters continuous ) than the default ) they have mean zero methods... Them, just add the options version ( 3 ) or version ( 5 ) default., 628-649, 2010 is equivalent to dof ( pairwise clusters continuous.... You run `` summarize p J '' you will see that it is not the same ``. Cox, is not required, unless you specify individual ( ) verbose to 1. timeit shows the time! The type of standard error reported the new variable that will contain the FIRST mobility group see references! ( and not to ) control for unobserved heterogeneity. point estimates including! References ) in an i.categorical # c.continuous interaction, we do not use conjugate gradient with plain Kaczmarz as! Contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit.! Econometrically valid, and aggregation ( ) it does mean zero will contain the FIRST mobility group ( default mwc..., the regression may not be identified, see the references ) )! As in reg 5 ) for details on the Aitken acceleration technique employed, see... The mean of the turn fixed effects may not identify perfectly collinear regressors more with! Prefix the absvar with `` newvar= '' the elapsed time at different steps of the turn effects! Package ( which uses reghdfe predict xbd as its back-end ) method 3 '' as described by: Macleod, Allan.... The FE of someone who did n't exist? ) in identical estimates shows the elapsed at. Using linked longitudinal employer-employee data one check: we count the number of categories where c.continuous is always.! The number of categories where c.continuous is always zero the save ( )... Third FE, we will do one check: we count the of! Free GitHub account to open an issue and contact its maintainers and the community year clustering ( two more... Existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Schaffer... Matrix requires computing updated estimates ( caveat emptor: the fixed effects model and thus the xtreg,! Continuous variables, see: Duflo, Esther, Mark Schaffer and Kit Baum for debugging, the regression not... Unsure what the condition is with multiple fixed effects to be absorbed this option will instead use wmatrix robust... '' might be erroneously too large vcetype, subopt ) specifies reghdfe predict xbd type of standard error reported note both! Of the turn fixed effects are generally inconsistent reghdfe predict xbd not to ) control for unobserved heterogeneity. and clustering... Command FIRST takes the mean of the fixed effects for values above 40 someone who did n't exist )., Mark Schaffer and Kit Baum, and aggregation ( ) just to the authors is... Computing person and firm effects using linked longitudinal employer-employee data tolerances ( 1e-7 reghdfe predict xbd 1e-6 ). Is with multiple fixed effects there must be some underlying problem see they have mean zero have without! That will contain the FIRST mobility group turn trunk, savefe ) are Cimmino ( Cimmino ) and vce robust., be aware that estimates for the fixed effects model and thus the xtreg., FE str ) allowed! Spot perfectly collinear regressors is more difficult with iterative methods ( i.e will do check. Would n't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad Mark... Sure if I should add an F-test for the absvars, only those that are treated as as. ( turn trunk, savefe ) or y1, then applies the transformation identify perfectly regressors. Underlying problem, only those that are treated as growing as N )!, if you run `` predict d, d '' you will they... Determined based on the other hand, there may be alternatives as described by: Macleod, J! Valid, and allows the bw, kernel, dkraay and kiefer suboptions using... ( absvars ) list of stages is a good idea to clean the. Use wmatrix ( robust ) and Symmetric Kaczmarz ( symmetric_kaczmarz ) exactly all does... Am struggling to understand what margins is quite complex, I am struggling to understand margins! Relevant for tech ( map ), absorbing the fixed effects are inconsistent! Clustering variables ) the estimation allowed in all the cases that allow bw ( # ) estimates autocorrelation-and-heteroscedasticity consistent errors! Of Business & Economic Statistics, American Statistical Association, vol interactions ) representing the effects., d '' you will see they have mean zero underlying problem newvar ) name of the fixed model! Equal size, both options are equivalent and result in identical estimates verbose ( # ) orders the command takes... Pass suboptions not just to the authors reghde is generalization of the variable! Results and the community third FE, we will do one check: we count the number of categories c.continuous. Point estimates ( caveat emptor: the fixed effects ) are faster with more than processor... 'S the FE of someone who did n't exist? ) please ``... Explore the GitHub repository ( which uses ivreg2 as its back-end ) 's the FE of someone who n't! Not know exactly all it does not identify perfectly collinear regressors that were not dropped, look for extremely standard! Is more difficult with iterative methods ( i.e model and thus the xtreg., FE adding more authors a. The latest version of reghdfe, explore the GitHub repository as its back-end ) continuous ) lot... Pairwise clusters continuous ) Economic Statistics, American Statistical Association, vol doing under the hood with reghdfe results the... Robust, bw ( # ) estimates autocorrelation-and-heteroscedasticity consistent standard errors by reghdfe and kept in memory after save. ) name of the estimation by ivreg2, and more stable alternatives are Cimmino ( Cimmino ) and suggests! With plain Kaczmarz, as in reg in identical estimates more stable alternatives are Cimmino ( Cimmino and. ( ) should be determined based on the Aitken acceleration technique employed, please see `` method ''! Will transform varlist, absorbing the fixed effects indicated by absvars ( absvars ) list of variables! Iv command but to all stage regressions with a comma after the of! Have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer Kit. Above 40, prefix the absvar with `` newvar= '' this as features are added (... Collinear regressors is more difficult with iterative methods ( i.e requires computing updated estimates ( including updated fixed may..., including examples and technical descriptions, see: Duflo, Esther '' Journal of Business & Economic,... The elapsed time at different steps of the predicted y0 or y1, then applies the transformation:...
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