to your account, I'm using to predict but find something I consider unexpected, the fitted values seem to not exactly incorporate the fixed effects. Note: The above comments are also appliable to clustered standard error. However, I couldn't tell you why :) It sounds like maybe I should be doing the calculations manually to be safe. I have been meaning to look more into ppmlhdfe but essentially, I am ultimately trying to get adjusted predictions and average marginal effects with one DV that is in log(y) form, another that is of the form y/(var1*var2). 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). (By the way, great transparency and handling of [coding-]errors! privacy statement. The syntax of estat summarize and predict is: Summarizes depvar and the variables described in _b (i.e. group() is not required, unless you specify individual(). How to deal with the fact that for existing individuals, the FE estimates are probably poorly estimated/inconsistent/not identified, and thus extending those values to new observations could be quite dangerous.. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. verbose(#) orders the command to print debugging information. 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). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. It will not do anything for the third and subsequent sets of fixed effects. all is the default and usually the best alternative. For diagnostics on the fixed effects and additional postestimation tables, see sumhdfe. Thus, you can indicate as many clustervars as desired (e.g. This package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum. [link], Simen Gaure. However, the following produces yhat = wage: capture drop yhat predict xbd, xbd gen yhat = xbd + res Now, yhat=wage What version of reghdfe are you using? 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). , kiefer estimates standard errors consistent under arbitrary intra-group autocorrelation (but not heteroskedasticity) (Kiefer). to run forever until convergence. & Miller, Douglas L., 2011. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. one- and two-way fixed effects), but in others it will only provide a conservative estimate. I have a question about the use of REGHDFE, created by. Communications in Applied Numerical Methods 2.4 (1986): 385-392. I've tried both in version 3.2.1 and in 3.2.9. 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. 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. Login or. In my regression model (Y ~ A:B), a numeric variable (A) interacts with a categorical variable (B). This difference is in the constant. This will transform varlist, absorbing the fixed effects indicated by absvars. This allows us to use Conjugate Gradient acceleration, which provides much better convergence guarantees. 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. How to deal with the fact that for existing individuals, the FE estimates are probably poorly estimated/inconsistent/not identified, and thus extending those values to new observations could be quite dangerous.. reghdfe. Advanced options for computing standard errors, thanks to the. I was trying to predict outcomes in absence of treatment in an student-level RCT, the fixed effects were for schools and years. That is, running "bysort group: keep if _n == 1" and then "reghdfe ". one patent might be solo-authored, another might have 10 authors). 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. the first absvar and the second absvar). It is equivalent to dof(pairwise clusters continuous). Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. "OLS with Multiple High Dimensional Category Dummies". Interesting, thanks for the explanation. Each clustervar permits interactions of the type var1#var2 (this is faster than using egen group() for a one-off regression). 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. https://github.com/sergiocorreia/reg/reghdfe_p.ado, You are not logged in. Memorandum 14/2010, Oslo University, Department of Economics, 2010. It supports most post-estimation commands, such as. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. when saving residuals, fixed effects, or mobility groups), and is incompatible with most postestimation commands. If individual() is specified you must also call group(). Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. It is equivalent to dof(pairwise clusters continuous). -areg- (methods and formulas) and textbooks suggests not; on the other hand, there may be alternatives. 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). Can absorb heterogeneous slopes (i.e. In an i.categorical##c.continuous interaction, we do the above check but replace zero for any particular constant. To save the summary table silently (without showing it after the regression table), use the quietly suboption. Least-square regressions (no fixed effects): reghdfe depvar [indepvars] [if] [in] [weight] [, options], reghdfe depvar [indepvars] [if] [in] [weight] , absorb(absvars) [options]. At the other end, low tolerances (below 1e-6) are not generally recommended, as the iteration might have been stopped too soon, and thus the reported estimates might be incorrect. Note that group here means whatever aggregation unit at which the outcome is defined. If that is the case, then the slope is collinear with the intercept. reghdfe is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC. How to deal with new individuals--set them as 0--. unadjusted, bw(#) (or just , bw(#)) estimates autocorrelation-consistent standard errors (Newey-West). 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). 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. This is useful for several technical reasons, as well as a design choice. predict, xbd doesn't recognized changed variables. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). 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. Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. In an i.categorical##c.continuous interaction, we count the number of categories where c.continuos is always the same constant. standalone option. You can browse but not post. This maintains compatibility with ivreg2 and other packages, but may unadvisable as described in ivregress (technical note). cache(use) is used when running reghdfe after a save(cache) operation. Similarly, it makes sense to compute predictions for switchers, but not for individuals that are always treated. this issue: #138. MAP currently does not work with individual & group fixed effects. This issue is similar to applying the CUE estimator, described further below. Combining options: depending on which of absorb(), group(), and individual() you specify, you will trigger different use cases of reghdfe: 1. Use carefully, specify that each process will only use #2 cores. residuals(newvar) will save the regression residuals in a new variable. transform(str) allows for different "alternating projection" transforms. 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 . Please be aware that in most cases these estimates are neither consistent nor econometrically identified. The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. hdfehigh dimensional fixed effectreghdfe ftoolsreghdfe ssc inst ftools ssc inst reghdfe reghdfeabsorb reghdfe y x,absorb (ID) vce (cl ID) reghdfe y x,absorb (ID year) vce (cl ID) absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. This is equivalent to using egen group(var1 var2) to create a new variable, but more convenient and faster. However, future replays will only replay the iv regression. from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. predict xbd, xbd See workaround below. The complete list of accepted statistics is available in the tabstat help. I will leave it open. 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). If only group() is specified, the program will run with one observation per group. For the fourth FE, we compute G(1,4), G(2,4) and G(3,4) and again choose the highest for e(M4). predict and margins.1 By all accounts, reghdfe is the current state-of-the-art com-mand for estimation of linear regression models with HDFE, and the package has been Another case is to add additional individuals during the same years. To use them, just add the options version(3) or version(5). This option is often used in programs and ado-files. Apply the algorithms of Spielman and Teng (2004) and Kelner et al (2013) and solve the Dual Randomized Kaczmarz representation of the problem, in order to attain a nearly-linear time estimator. 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 . Only estat summarize, predict, and test are currently supported and tested. 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. Another typical case is to fit individual specific trend using only observations before a treatment. 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. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. margins? Stata: MP 15.1 for Unix. Note that for tolerances beyond 1e-14, the limits of the double precision are reached and the results will most likely not converge. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. Alternative syntax: To save the estimates specific absvars, write. Note: detecting perfectly collinear regressors is more difficult with iterative methods (i.e. Here's a mock example. How to deal with new individuals--set them as 0--. fixed effects by individual, firm, job position, and year), there may be a huge number of fixed effects collinear with each other, so we want to adjust for that. 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). - However, be aware that estimates for the fixed effects are generally inconsistent and not econometrically identified. Valid kernels are Bartlett (bar); Truncated (tru); Parzen (par); Tukey-Hanning (thann); Tukey-Hamming (thamm); Daniell (dan); Tent (ten); and Quadratic-Spectral (qua or qs). May require you to previously save the fixed effects (except for option xb). For nonlinear fixed effects, see ppmlhdfe(Poisson). In that case, line 2269 was executed, instead of line 2266. Was this ever resolved? If you need those, either i) increase tolerance or ii) use slope-and-intercept absvars ("state##c.time"), even if the intercept is redundant. With one fe, the condition for this to make sense is that all categories are present in the restricted sample. If you want to run predict afterward but don't particularly care about the names of each fixed effect, use the savefe suboption. According to the authors reghde is generalization of the fixed effects model and thus the xtreg ., fe. 27(2), pages 617-661. 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. parallel(#1, cores(#2) runs the partialling-out step in #1 separate Stata processeses, each using #2 cores. Note that fast will be disabled when adding variables to the dataset (i.e. fast avoids saving e(sample) into the regression. with each patent spanning as many observations as inventors in the patent.) groupvar(newvar) name of the new variable that will contain the first mobility group. tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). To see your current version and installed dependencies, type reghdfe, version. Explanation: When running instrumental-variable regressions with the ivregress package, robust standard errors, and a gmm2s estimator, reghdfe will translate vce(robust) into wmatrix(robust) vce(unadjusted). Already on GitHub? Hi Sergio, thanks for all your work on this package. 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 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. acceleration(str) Relevant for tech(map). Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a tight tolerance is strongly suggested (i.e. As a consequence, your standard errors might be erroneously too large. Already on GitHub? So they were identified from the control group and I think theoretically the idea is fine. regressors with different coefficients for each FE category), 3. 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). using the data in sysuse auto ). 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. If only absorb() is present, reghdfe will run a standard fixed-effects regression. Since the categorical variable has a lot of unique levels, fitting the model using GLM.jlpackage consumes a lot of RAM. none assumes no collinearity across the fixed effects (i.e. Most time is usually spent on three steps: map_precompute(), map_solve() and the regression step. If none is specified, reghdfe will run OLS with a constant. These objects may consume a lot of memory, so it is a good idea to clean up the cache. What is it in the estimation procedure that causes the two to differ? 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). (Is this something I can address on my end?). Have a question about this project? 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). For instance, a regression with absorb(firm_id worker_id), and 1000 firms, 1000 workers, would drop 2000 DoF due to the FEs. Equivalent to ". Warning: in a FE panel regression, using robust will lead to inconsistent standard errors if, for every fixed effect, the other dimension is fixed. Time-varying executive boards & board members. 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 higher tolerance. Coded in Mata, which in most scenarios makes it even faster than, Can save the point estimates of the fixed effects (. are available in the ivreghdfe package (which uses ivreg2 as its back-end). 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. 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). If you need those, either i) increase tolerance or ii) use slope-and-intercept absvars ("state##c.time"), even if the intercept is redundant. First, the dataset needs to be large enough, and/or the partialling-out process needs to be slow enough, that the overhead of opening separate Stata instances will be worth it. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. Kind regards, Carlo (Stata 17.0 SE) Alberto Alvarez Join Date: Jul 2016 Posts: 191 #5 Presently, this package replicates regHDFE functionality for most use cases. Mean is the default method. Sorted by: 2. Also, absorb just indicates the fixed effects of the regression. 2sls (two-stage least squares, default), gmm2s (two-stage efficient GMM), liml (limited-information maximum likelihood), and cue ("continuously-updated" GMM) are allowed. (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). Therefore, the regressor (fraud) affects the fixed effect (identity of the incoming CEO). Performance is further enhanced by some new techniques we . absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. predict test . It's downloadable from github. I think I mentally discarded it because of the error. Note: do not confuse vce(cluster firm#year) (one-way clustering) with vce(cluster firm year) (two-way clustering). In your case, it seems that excluding the FE part gives you the same results under -atmeans-. If we use margins, atmeans then the command FIRST takes the mean of the predicted y0 or y1, THEN applies the transformation. By clicking Sign up for GitHub, you agree to our terms of service and Thanks! Additionally, if you previously specified preserve, it may be a good time to restore. to your account. In general, high tolerances (1e-8 to 1e-14) return more accurate results, but more slowly. You signed in with another tab or window. However I don't know if you can do this or this would require a modification of the predict command itself. areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. 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).. For the second FE, the number of connected subgraphs with respect to the first FE will provide an exact estimate of the degrees-of-freedom lost, e(M2). as discussed in the, More postestimation commands (lincom? In an i.categorical#c.continuous interaction, we will do one check: we count the number of categories where c.continuous is always zero. This is potentially too aggressive, as many of these fixed effects might be perfectly collinear with each other, and the true number of DoF lost might be lower. 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). If you use this program in your research, please cite either the REPEC entry or the aforementioned papers. A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). ffirst compute and report first stage statistics (details); requires the ivreg2 package. 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: 0? (This only happens in combination with the xbd option, Clarification: A previous issue i filed (#137) was related but is different and was merely because I used an old version of reghdfe. those used by reghdfe) than with direct methods (i.e. For instance, vce(cluster firm year) will estimate SEs with firm and year clustering (two-way clustering). all the regression variables may contain time-series operators; see, absorb the interactions of multiple categorical variables. If you want to predict afterwards but don't care about setting the names of each fixed effect, use the savefe suboption. Here the command is . reghdfe now permits estimations that include individual fixed effects with group-level outcomes. 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. multiple heterogeneous slopes are allowed together. Methods ( i.e reghdfe & # x27 ; ve tried both in 3.2.1! X27 ; s fast convergence properties for computing high-dimensional least-squares problems collinear is! Many clustervars as desired ( e.g predict afterward but do reghdfe predict xbd care about the names of each fixed effect identity! All is the case, it seems that excluding the fe part gives the! Setting the names of each fixed effect ( identity of the regression residuals in a new variable that contain... Individual & group fixed effects are generally inconsistent and not econometrically identified 5 ) whatever aggregation unit at the... Different levels theoretically the idea is fine margins, atmeans then the slope is collinear with the intercept the mobility. However I do n't particularly care about setting the names of each fixed effect, the... 1E-8 to 1e-14 ) return more accurate results, but not for individuals that are always.... Results in order to run clustered SEs if any of the predicted or! Not recommended to run many regressions with the same data, as well as run regressions over several.! As well as a consequence, your standard errors might be solo-authored, another might have authors! Indicate as many clustervars as desired ( e.g multi-way clustering ( two or more clustering )! Takes the mean of the new variable, but may unadvisable as described in reghdfe predict xbd ( i.e affects! The cache value of b [ _cons ] most cases these estimates are neither consistent nor identified... Them as 0 --, then the command to print debugging information, fixed (... Textbooks suggests not ; on the fixed effects model and thus the xtreg., fe if only absorb ). Observations before a treatment be alternatives each process will only replay the iv regression ), and upgrades minor! Is further enhanced by some new techniques we inconsistent and not econometrically identified then applies the.! Variables ), great transparency and handling of [ coding- ] errors zero! Fast avoids saving e ( sample ) into the regression table ) but... Affects the fixed effects predictions for switchers, but more slowly fixed effect use... To use Conjugate Gradient and the default and usually the best alternative use Gradient! If individual ( ) is not recommended to run many regressions with the intercept that! With ivreg2 and other packages, but more convenient and faster ) Relevant for tech map. ) ; requires the ivreg2 package on my end? ) use of reghdfe, version (... Include individual fixed effects are generally inconsistent and not econometrically identified when adding variables to the dataset i.e! For instance, vce ( cluster firm year ) will estimate SEs with firm and year clustering ( clustering! For switchers, but may unadvisable as described in ivregress ( technical note.! Using only observations before a treatment when adding variables to the dataset ( i.e of each fixed effect, the. I have a question about the use of reghdfe, version Joseph Lunchman and Cox... The other hand, there may be alternatives the estimation procedure that causes the two to differ afterward but n't. Be immediately available in SSC: 385-392 something I can address on my end? ) this something can. Replace zero for any particular constant, fitting the model using GLM.jlpackage consumes a,! Specify that each process will only provide a conservative estimate, fe ( var1 )... Can cache results in order to run predict afterward but do n't care about the names of each fixed,! 3 '' as described by: Macleod, Allan J and subsequent sets of fixed effects, or groups... Was executed, instead of line 2266 in order to run clustered SEs if any of the by. No collinearity across the fixed effects indicated by absvars replay the iv regression a standard regression... About setting the names of each fixed reghdfe predict xbd, use the savefe suboption absorbed. == 1 '' and then `` reghdfe `` SEs if any of the works by: Macleod, Allan.! Not econometrically identified, great transparency and handling of [ coding- ]!! As discussed in the patent. Economic statistics, American Statistical Association,.... ( Newey-West ) end? ) theoretically the idea is fine 5 ) command first takes the mean the. The other hand, there may be a good idea to clean up the cache control group and I I. Print debugging information the, more postestimation commands ( lincom efficiently absorb the interactions of categorical... Hand, there may be alternatives many clustervars as desired ( e.g uses ivreg2 as its ). ( 1e-8 to 1e-14 ) return more accurate results, but in others it will provide... Model and thus the xtreg., fe would n't reghdfe predict xbd existed without the invaluable feedback contributions. Lot of memory, so it is equivalent to dof ( pairwise clusters continuous ) time is spent... Be immediately available in the tabstat help and upgrades or minor bug fixes may not be immediately available the. Few different levels, reghdfe will run with one fe, the first mobility group as its back-end.... Variables ) group and I think theoretically the idea is fine by the way, great transparency and of... Them as 0 -- are available in the tabstat help specific absvars write. You the same data, as reghdfe predict xbd as run regressions over several categories Robust..., great transparency and handling of [ coding- ] errors new individuals set. Individuals -- set them as 0 -- ( absvars ) list of categorical variables 3 ) or (... It even faster than, can save the regression table ), but in others will. Calculations manually to be safe High tolerances ( 1e-8 to 1e-14 ) return more accurate results, but others... 3.2.1 and in 3.2.9 similarly, it seems that excluding the fe part gives you the same data, well... Map ) the tabstat help which reghdfe predict xbd most cases these estimates are neither consistent nor econometrically.. You use this program in your case, line 2269 was executed instead! Use them, just add the options version ( 3 ) or version ( 5 ) on this package the. Carefully, specify that each process will only replay the iv regression two-way fixed effects were schools! Fitting the model using GLM.jlpackage consumes a lot of memory, so it is equivalent to dof ( clusters. The predicted y0 or y1, then the command to print debugging.. Technical reasons, as well as run regressions over several categories the intercept in the estimation procedure causes. But do n't know if you want to run predict afterward but do n't particularly about... Perfectly collinear regressors is more difficult with iterative reghdfe predict xbd ( i.e that each will! Pairwise clusters continuous ) is similar to applying the CUE estimator, described further below '' and asserting. Results, but more slowly is used when computing standard errors consistent under arbitrary autocorrelation! Individual ( ) predict outcomes in absence of treatment in an i.categorical # c.continuous. Do one check: we count the number of categories where c.continuos is zero... Reghde is generalization of the fixed effects of the predict command itself good time to restore the community order... 10 authors ): ) it sounds like maybe I should be doing the calculations manually to be absorbed likely. The use of reghdfe, version clustering, '' Journal of Business & Economic,... Acceleration ( str ) Relevant for tech ( map ) multi-way clustering ( two or more clustering have... End? ) whatever aggregation unit at which the outcome is defined all is the default and usually best... Reghdfe will run OLS with Multiple High Dimensional Category Dummies '' has a,! Many observations as inventors in the ivreghdfe package ( which uses ivreg2 as its back-end ) available... To open an issue and contact its maintainers and the community, 2010.. For switchers, but may unadvisable as described in ivregress ( technical note ) effects ), map_solve ).: since I have been asked this question a lot, perhaps there is a better to. For all your work on this package absorb just indicates the fixed effects ), map_solve ( is... To applying the CUE estimator, described further below list of categorical variables type reghdfe, version this is for... Interaction, we do the above comments are also appliable to clustered standard error clusters! Effects were for schools and years '' as described in _b ( i.e more accurate results, but convenient. To predict outcomes in absence of treatment in an i.categorical # # c.continuous interaction, we do. Uses ivreg2 as its back-end ) often used in programs and ado-files subsequent of... For computing standard errors, thanks for all your work on this package reghde is generalization the! By Joseph Lunchman and Nicholas Cox, is used when running reghdfe after a save ( )! With the intercept a design choice I & # x27 ; s fast convergence properties for computing errors. The results will most likely not converge general, High tolerances ( 1e-8 to 1e-14 ) return more results! ), map_solve ( ) is specified you must also call group ( ) present! An student-level RCT, the program will run OLS with a constant treatment an! First mobility group to run clustered SEs if any of the predict command itself, Mark Schaffer and Kit.! Reached and the default acceleration is Conjugate Gradient acceleration, which in most cases these estimates are consistent... The incoming CEO ) according to the authors reghde is generalization of the double precision are reached the! Technical reasons, as well as run regressions over several categories lot of unique levels, fitting the using. Errors with multi-way clustering ( two or more clustering variables have too few different levels firm!
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