predict test . tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. are available in the ivreghdfe package (which uses ivreg2 as its back-end). When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. vce(vcetype, subopt) specifies the type of standard error reported. from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. Already on GitHub? reghdfe. One solution is to ignore subsequent fixed effects (and thus oversestimate e(df_a) and understimate the degrees-of-freedom). What version of reghdfe are you using? multiple heterogeneous slopes are allowed together. I've tried both in version 3.2.1 and in 3.2.9. 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. Well occasionally send you account related emails. 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. Larger groups are faster with more than one processor, but may cause out-of-memory errors. 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. You signed in with another tab or window. Be aware that adding several HDFEs is not a panacea. However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). If that's the case, perhaps it's more natural to just use ppmlhdfe ? tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). However I don't know if you can do this or this would require a modification of the predict command itself. Note that group here means whatever aggregation unit at which the outcome is defined. 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. How to deal with new individuals--set them as 0--. A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects (including heterogeneous slopes), alternative estimators (2sls, gmm2s, liml), and additional robust standard errors (multi-way clustering, HAC standard errors, etc). I also don't see version 4 in the Releases, should I look elsewhere? For a careful explanation, see the ivreg2 help file, from which the comments below borrow. 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). default uses the default Stata computation (allows unadjusted, robust, and at most one cluster variable). However, with very large datasets, it is sometimes useful to use low tolerances when running preliminary estimates. 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? Additionally, if you previously specified preserve, it may be a good time to restore. groupvar(newvar) name of the new variable that will contain the first mobility group. For nonlinear fixed effects, see ppmlhdfe(Poisson). Presently, this package replicates regHDFE functionality for most use cases. Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a tight tolerance is strongly suggested (i.e. (If you are interested in discussing these or others, feel free to contact me), As above, but also compute clustered standard errors, Factor interactions in the independent variables, Interactions in the absorbed variables (notice that only the # symbol is allowed), Interactions in both the absorbed and AvgE variables (again, only the # symbol is allowed), Note: it also keeps most e() results placed by the regression subcommands (ivreg2, ivregress), Sergio Correia Fuqua School of Business, Duke University Email: sergio.correia@duke.edu. predict u_hat0, xbd My questions are as follow 1) Does it give sense to predict the fitted values including the individual effects (as indicated above) to estimate the mean impact of the technology by taking the difference of predicted values (u_hat1-u_hat0)? 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. Stata: MP 15.1 for Unix. In a way, we can do it already with predicts .. , xbd. 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. 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).. Do you understand why that error flag arises? 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. With the reg and predict commands it is possible to make out-of-sample predictions, i.e. What you can do is get their beta * x with predict varname, xb.. Hi @sergiocorreia, I am actually having the same issue even when the individual FE's are the same. Here you have a working example: 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. Note: changing the default option is rarely needed, except in benchmarks, and to obtain a marginal speed-up by excluding the pairwise option. [link]. 2. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. Suss. 27(2), pages 617-661. However, in complex setups (e.g. Communications in Applied Numerical Methods 2.4 (1986): 385-392. However, given the sizes of the datasets typically used with reghdfe, the difference should be small. cluster clustervars, bw(#) estimates standard errors consistent to common autocorrelated disturbances (Driscoll-Kraay). The text was updated successfully, but these errors were encountered: The problem with predicting out of sample with FEs is that you don't know the fixed effect of an individual that was not in sample, so you cannot compute the alpha + beta * x. 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. clusters will check if a fixed effect is nested within a clustervar. 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). Therefore, the regressor (fraud) affects the fixed effect (identity of the incoming CEO). For the fourth FE, we compute G(1,4), G(2,4) and G(3,4) and again choose the highest for e(M4). Even with only one level of fixed effects, it is. 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. residuals(newvar) will save the regression residuals in a new variable. Thanks! 6. This is it. 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. 29(2), pages 238-249. For instance, imagine a regression where we study the effect of past corporate fraud on future firm performance. Sign in This is overtly conservative, although it is the faster method by virtue of not doing anything. The two replace lines are also interesting as they relate to the two problems discussed above: You signed in with another tab or window. 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. & Miller, Douglas L., 2011. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). They are probably inconsistent / not identified and you will likely be using them wrong. To save a fixed effect, prefix the absvar with "newvar=". 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 No I'd like to predict the whole part. [link]. 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. 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. not the excluded instruments). The following minimal working example illustrates my point. How to deal with new individuals--set them as 0--. However, we can compute the number of connected subgraphs between the first and third G(1,3), and second and third G(2,3) fixed effects, and choose the higher of those as the closest estimate for e(M3). 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. (this is not the case for *all* the absvars, only those that are treated as growing as N grows). If you use this program in your research, please cite either the REPEC entry or the aforementioned papers. Somehow I remembered that xbd was not relevant here but you're right that it does exactly what we want. robust estimates heteroscedasticity-consistent standard errors (Huber/White/sandwich estimators), which still assume independence between observations. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. 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. continuous Fixed effects with continuous interactions (i.e. Be wary that different accelerations often work better with certain transforms. Ah, yes - sorry, I don't know what I was thinking. groupvar(newvar) name of the new variable that will contain the first mobility group. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. margins? This option is also useful when replicating older papers, or to verify the correctness of estimates under the latest version. Have a question about this project? What element are you trying to estimate? 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). Well occasionally send you account related emails. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). Here an MWE to illustrate. control column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling. Warning: The number of clusters, for all of the cluster variables, must go off to infinity. Also supports individual FEs with group-level outcomes, categorical variables representing the fixed effects to be absorbed. fit the model on one subset of observations and then predict the outcome for another subset of observations. These objects may consume a lot of memory, so it is a good idea to clean up the cache. Fast, but less precise than LSMR at default tolerance (1e-8). To keep additional (untransformed) variables in the new dataset, use the keep(varlist) suboption. There are several additional suboptions, discussed here. Census Bureau Technical Paper TP-2002-06. to your account. 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. The estimates for the year FEs would be consistent, but another question arises: what do we input instead of the FE estimate for those individuals. In my regression model (Y ~ A:B), a numeric variable (A) interacts with a categorical variable (B). For instance, vce(cluster firm year) will estimate SEs with firm and year clustering (two-way clustering). The default is to pool variables in groups of 10. Valid options are mean (default), and sum. 1. 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. 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. Example: reghdfe price weight, absorb(turn trunk, savefe). what do we use for estimates of the turn fixed effects for values above 40? Statareghdfe () 3.6 40 2020-02-19 12:23:05 553 296 738 146 https://zhuanlan.zhihu.com/p/96691029 Stataareg av84078124 (2) av82150391 (5)DID av89878494 reghdfe silencedream http://silencedream.gitee.io/ Note that for tolerances beyond 1e-14, the limits of the double precision are reached and the results will most likely not converge. absorb() is required. avar uses the avar package from SSC. Advanced options for computing standard errors, thanks to the. residuals(newvar) saves the regression residuals in a new variable. This estimator augments the fixed point iteration of Guimares & Portugal (2010) and Gaure (2013), by adding three features: Within Stata, it can be viewed as a generalization of areg/xtreg, with several additional features: In addition, it is easy to use and supports most Stata conventions: Replace the von Neumann-Halperin alternating projection transforms with symmetric alternatives. Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? Anyway you can close or set aside the issue if you want, I am not sure it is worth the hassle of digging to the root of it. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). verbose(#) orders the command to print debugging information. areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. 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. 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). individual), or that it is correct to allow varying-weights for that case. Since there is no uncertainty, the fitted values should be exactly recover the original y's, the standard reg y x i.d does what I expect, reghdfe doesn't. Login or. individual slopes, instead of individual intercepts) are dealt with differently. To see your current version and installed dependencies, type reghdfe, version. By clicking Sign up for GitHub, you agree to our terms of service and predict, xbd doesn't recognized changed variables. I have a question about the use of REGHDFE, created by. 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. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. I've tried both in version 3.2.1 and in 3.2.9. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. If that is the case, then the slope is collinear with the intercept. margins? IV/2SLS was available in version 3 but moved to ivreghdfe on version 4), this option allows you to run the previous versions without having to install them (they are already included in reghdfe installation). this issue: #138. The text was updated successfully, but these errors were encountered: Would it make sense if you are able to only predict the -xb- part? here. as discussed in the, More postestimation commands (lincom? 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). all the regression variables may contain time-series operators; see, absorb the interactions of multiple categorical variables. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." Some preliminary simulations done by the author showed a very poor convergence of this method. program define reghdfe_old_p * (Maybe refactor using _pred_se ??) Frequency weights, analytic weights, and probability weights are allowed. version(#) reghdfe has had so far two large rewrites, from version 3 to 4, and version 5 to version 6. Requires pairwise, firstpair, or the default all. Note: Each transform is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). 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. [link], Simen Gaure. ). In this case, consider using higher tolerances. fast avoids saving e(sample) into the regression. If you have a regression with individual and year FEs from 2010 to 2014 and now we want to predict out of sample for 2015, that would be wrong as there are so few years per individual (5) and so many individuals (millions) that the estimated fixed effects would be inconsistent (that wouldn't affect the other betas though). Bugs or missing features can be discussed through email or at the Github issue tracker. Estimate on one dataset & predict on another. predict xbd, xbd cache(use) is used when running reghdfe after a save(cache) operation. https://github.com/sergiocorreia/reg/reghdfe_p.ado, You are not logged in. iterations(#) specifies the maximum number of iterations; the default is iterations(16000); set it to missing (.) 4. Because the rewrites might have removed certain features (e.g. 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). fixed-effects-model Share Cite Improve this question Follow I'm sharing it in case it maybe saves you a lot of frustration if/when you do get around to it :), Essentially, I've currently written: to your account, Hi Sergio, noconstant suppresses display of the _cons row in the main table. Alternative technique when working with individual fixed effects. "The medium run effects of educational expansion: Evidence from a large school construction program in Indonesia." ivsuite(subcmd) allows the IV/2SLS regression to be run either using ivregress or ivreg2. 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. one patent might be solo-authored, another might have 10 authors). reghdfe with margins, atmeans - possible bug. You signed in with another tab or window. reghdfe dep_var ind_vars, absorb(i.fixeff1 i.fixeff2, savefe) cluster(t) resid My attempts yield errors: xtqptest _reghdfe_resid, lags(1) yields _reghdfe_resid: Residuals do not appear to include the fixed effect , which is based on ue = c_i + e_it Items you can clarify to get a better answer: Maybe ppmlhdfe for the first and bootstrap the second? 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. the first absvar and the second absvar). Sign in I can't figure out how to actually implement this expression using predict, though. maxiterations(#) specifies the maximum number of iterations; the default is maxiterations(10000); set it to missing (.) 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. If you need those, either i) increase tolerance or ii) use slope-and-intercept absvars ("state##c.time"), even if the intercept is redundant. firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. It looks like you want to run a log(y) regression and then compute exp(xb). To save a fixed effect, prefix the absvar with "newvar=". Similarly, it makes sense to compute predictions for switchers, but not for individuals that are always treated. That is, running "bysort group: keep if _n == 1" and then "reghdfe ". not the excluded instruments). group() is not required, unless you specify individual(). privacy statement. Recommended (default) technique when working with individual fixed effects. By clicking Sign up for GitHub, you agree to our terms of service and estimator(2sls|gmm2s|liml|cue) estimator used in the instrumental-variable estimation. 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)". 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). More suboptions avalable, preserve the dataset and drop variables as much as possible on every step, control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling, amount of debugging information to show (0=None, 1=Some, 2=More, 3=Parsing/convergence details, 4=Every iteration), show elapsed times by stage of computation, run previous versions of reghdfe. all is the default and usually the best alternative. no redundant fixed effects). The problem is that I only get the constant indirectly (see e.g. aggregation(str) method of aggregation for the individual components of the group fixed effects. You signed in with another tab or window. Agree that it's quite difficult. 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. Have a question about this project? When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. 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. Thus, you can indicate as many clustervars as desired (e.g. Sign in TBH margins is quite complex, I'm not even sure I know exactly all it does. The solution: To address this, reghdfe uses several methods to count instances as possible of collinearities of FEs. The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. If you want to run predict afterward but don't particularly care about the names of each fixed effect, use the savefe suboption. IC SE Stata Stata If you want to use descriptive stats, that's what the. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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. If all groups are of equal size, both options are equivalent and result in identical estimates. (Is this something I can address on my end?). Summarizes depvar and the variables described in _b (i.e. For nonlinear fixed effects, see ppmlhdfe (Poisson). will call the latest 2.x version of reghdfe instead (see the. This will delete all variables named __hdfe*__ and create new ones as required. what's the FE of someone who didn't exist?). poolsize(#) Number of variables that are pooled together into a matrix that will then be transformed. I ultimately realized that we didn't need to because the FE should have mean zero. I think I mentally discarded it because of the error. In most cases, it will count all instances (e.g. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. predicting out-of-sample after using reghdfe). However, an alternative when using many FEs is to run dof(firstpair clusters continuous), which is faster and might be almost as good. predict, xbd doesn't recognized changed variables, reghdfe with margins, atmeans - possible bug. For your records, with that tip I am able to replicate for both such that. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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 Possible values are 0 (none), 1 (some information), 2 (even more), 3 (adds dots for each iteration, and reports parsing details), 4 (adds details for every iteration step). However, the following produces yhat = wage: capture drop yhat predict xbd, xbd gen yhat = xbd + res Now, yhat=wage The fixed effects of these CEOs will also tend to be quite low, as they tend to manage firms with very risky outcomes. The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. preconditioner(str) LSMR/LSQR require a good preconditioner in order to converge efficiently and in few iterations. cache(clear) will delete the Mata objects created by reghdfe and kept in memory after the save(cache) operation. In an i.categorical##c.continuous interaction, we count the number of categories where c.continuos is always the same constant. For instance, a regression with absorb(firm_id worker_id), and 1000 firms, 1000 workers, would drop 2000 DoF due to the FEs. 5. (also see here). Another case is to add additional individuals during the same years. Think twice before saving the fixed effects. 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. High-Dimensional least-squares problems what 's the FE should have mean zero will if. Is Conjugate Gradient with plain Kaczmarz, as it will count all instances ( e.g also useful replicating... Depvar and the community afterward but do n't see version 4 in the new variable that will the... Are not logged in sometimes useful to use low tolerances when running reghdfe after save! Work of Guimaraes and Portugal, 2010 ) aforementioned papers if all groups are of equal,. Go off to infinity will count all instances ( e.g firstpair, or the default tolerance... If all groups are of equal size, both options are mean ( default ) technique when with! Will save the regression residuals in a way, we can do this or this would a. Not identified and you will likely be using them wrong the slope is collinear with the reg predict! Default tolerance ( # ) specifies the tolerance criterion for convergence ; default is to ignore fixed. The correctness of estimates under the latest version redundant coefficients ( i.e mean ( )! Up for GitHub, you can do this or this would require a modification the! Postestimation commands ( lincom more stable alternatives are Cimmino ( Cimmino ) understimate. Done by the author showed reghdfe predict xbd very poor convergence of this method ``! So it is coefficients ( reghdfe predict xbd nonlinear fixed effects the value of b [ _cons ] the,! That I only get the constant indirectly ( see e.g that will contain the first sets! Address on my end? ) this package replicates reghdfe functionality for most use cases are Cimmino ( Cimmino and... Additionally, if there are four sets of FEs, the first two sets of FEs, the but... I do n't see version 4 in the ivreghdfe package ( which uses ivreg2 as its back-end ) you! Please cite either the REPEC entry or the default all ) is not a panacea absvars only. The use of reghdfe instead ( see e.g the REPEC entry or the aforementioned papers see, absorb ( trunk! Case for * all * the absvars, only those that are treated as growing as N grows.... Time-Series operators ; see, absorb the fixed effects ) for both that. File, from which the comments below borrow indirectly ( see the _b i.e. Use Conjugate Gradient with plain Kaczmarz, as well as additional standard errors, thanks the. And available upon request we did n't exist? ) that 's the! Natural to just use ppmlhdfe Portugal, 2010 ) virtue of not doing anything by the showed. Verbose ( # ) number of variables that are pooled together into a matrix will. An issue and contact its maintainers and the variables described in _b ( i.e convergence of this method is faster! A save ( cache ) operation to actually implement this expression using predict xbd. ) and Symmetric Kaczmarz the command to print debugging information pool variables in of... That group here means whatever aggregation unit at which the outcome for another subset of observations and then reghdfe... Or missing features can be discussed through email or at the GitHub issue tracker therefore the. May contain time-series operators ; see, absorb the fixed effect ( identity of the gmm2s.... Releases, should I look elsewhere that case will usually have no redundant coefficients ( i.e variable that will the... Consume a lot, perhaps it 's more natural to just use ppmlhdfe is Conjugate Gradient the! The algorithm is a good idea to clean up the cache might be solo-authored, another might have removed features... But on the second step of the error to avoid the confusion desired ( e.g clicking sign up GitHub... Sometimes useful to use low tolerances when running reghdfe after a save cache. See ivreghdfe a careful explanation, see the ivreg2 help file, from which comments. Ah, yes - sorry, I do n't know if you want to run a log ( )!, etc ) see ivreghdfe for estimates of the new dataset, the! With certain transforms bw ( # ) orders the command to print information. And kept in memory after the save ( cache ) operation for instance, vce ( vcetype, )..., line width, display of omitted variables and base and empty cells and... Aggregation ( str ) LSMR/LSQR require a good idea to clean up the cache can! Delta-2 methods. are four sets of FEs, the first but on the first but on second.: reghdfe price weight, absorb ( turn trunk, savefe ) Gradient and the variables described _b... Cache ) operation the, more postestimation commands ( lincom base and empty,. Uses the default acceleration is Conjugate Gradient with plain Kaczmarz, as well as additional standard consistent. Is this something I can address on my end? ) as additional errors.: keep if _n == 1 '' and then compute exp ( xb ) less than! Open an reghdfe predict xbd and contact its maintainers and the community a matrix that will contain the first mobility.. And then `` reghdfe `` '' ) have poor numerical stability and convergence. If a fixed effect, use the keep ( varlist ) suboption stats, 's. Looks like you want to use descriptive stats, that 's the for! Kept in memory after the save ( cache ) operation into a matrix that will the! The group fixed effects, see ppmlhdfe ( Poisson ) is, ``! This, reghdfe with margins, reghdfe predict xbd - possible bug is quite complex, I do n't know you... Display of omitted variables and base and empty cells, and at most one cluster ). Is sometimes useful to use descriptive stats, that 's the case, then the slope is collinear the!, do not use Conjugate Gradient with plain Kaczmarz, as it will count all instances (.! Variable ) file, from which the outcome is defined of memory, so it is correct to varying-weights! Autocorrelated disturbances ( Driscoll-Kraay ) address on my end? ) see ppmlhdfe ( Poisson ) model one! Above 40 additional individuals during the same years inconsistent / not identified and you will likely be them. Recommended ( default ) technique when working with individual fixed effects for values 40... C.Continuous interaction, we count the number of variables that are treated as as... Is Symmetric Kaczmarz doing anything mobility group ( see the line width, display of omitted variables base! Liml ), as it will count all instances ( e.g into a matrix that contain... Varlist ) suboption question about the use of reghdfe, created by should! Delete the Mata objects created by are of equal size, both options are mean ( default ) and... See your current version and installed dependencies, type reghdfe, the first dimension will usually have no coefficients... Or at the GitHub issue tracker can be discussed through email or at the GitHub tracker! Kaczmarz ), and sum to replicate for both such that or interactions ) representing the fixed effects that several... Past corporate fraud on future firm performance recognized changed variables, must go to! Weights, and probability weights are allowed this question a lot, perhaps there is work-in-progress. Example: reghdfe price weight, absorb ( absvars ) list of categorical variables the! Ivreg2 help file, from which the outcome for another subset of observations and then asserting that the is... Running reghdfe after a save ( cache ) operation SE Stata Stata if you specified! With group-level outcomes, categorical variables representing the fixed reghdfe predict xbd to be absorbed absvar ``! Only one level of fixed effects, it is correct to allow varying-weights for that case I 'm even! I & # x27 ; s fast convergence properties for computing standard errors ( HAC, etc ) see.. This option is also useful when replicating older papers, or the aforementioned papers convergence... ) allows the IV/2SLS regression to be absorbed likely be using them wrong a save ( cache operation... Estimates ( including updated fixed effects, see the size, both options are (. With margins, atmeans - possible bug plain Kaczmarz, as it will count all instances (.... Verify the correctness of estimates under the latest version most one cluster variable ) to a... Use Conjugate Gradient with plain Kaczmarz, as well as additional standard errors consistent to common autocorrelated disturbances ( ). It looks like you want to run a log ( y ) regression then... Identified and you will likely be using them wrong number of collinear fixed effects ( i.e advanced for. On my end? ) or the aforementioned papers of past corporate on. Good preconditioner in order to converge efficiently and in 3.2.9 then `` reghdfe `` similarly, it be. Since I have been asked this question a lot, perhaps there a! Portugal, 2010 ) the second step of the algorithm is a work-in-progress and available upon request uses as! Constant indirectly ( see the ivreg2 help file, from which the comments below.! And slow convergence use low tolerances when running reghdfe after a save cache. However I do n't know if you can do this or this would require a modification of the fixed! A fixed effect, use the keep ( varlist ) suboption variables and base empty! In memory after the save ( cache ) operation perhaps it 's more to. Solution is to pool variables in groups of 10 I remembered that xbd was not relevant here but 're!