reghdfe predict xbd

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. to your account, Hi Sergio, Already on GitHub? If that's the case, perhaps it's more natural to just use ppmlhdfe ? Note that parallel() will only speed up execution in certain cases. 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. poolsize(#) Number of variables that are pooled together into a matrix that will then be transformed. privacy statement. See workaround below. Have a question about this project? Supports two or more levels of fixed effects. Well occasionally send you account related emails. noheader suppresses the display of the table of summary statistics at the top of the output; only the coefficient table is displayed. 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 For simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of the estimation. This option does not require additional computations and is required for subsequent calls to predict, d. summarize(stats) this option is now part of sumhdfe. 15 Jun 2018, 01:48. 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.. These statistics will be saved on the e(first) matrix. Recommended (default) technique when working with individual fixed effects. Also, absorb just indicates the fixed effects of the regression. individual slopes, instead of individual intercepts) are dealt with differently. Warning: cue will not give the same results as ivreg2. 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. suboptions() options that will be passed directly to the regression command (either regress, ivreg2, or ivregress), vce(vcetype, subopt) specifies the type of standard error reported. REGHDFE: Distribution-Date: 20180917 The problem: without any adjustment, the degrees-of-freedom (DoF) lost due to the fixed effects is equal to the count of all the fixed effects. Mittag, N. 2012. At the other end, is not tight enough, the regression may not identify perfectly collinear regressors. reghdfe lprice i.foreign , absorb(FE = rep78) resid margins foreign, expression(exp(predict(xbd))) atmeans On a related note, is there a specific reason for what you want to achieve? 2. are dropped iteratively until no more singletons are found (see ancilliary article for details). residuals (without parenthesis) saves the residuals in the variable _reghdfe_resid (overwriting it if it already exists). 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). tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). year), and fixed effects for each inventor that worked in a patent. expression(exp( predict( xb + FE ) )). stages(list) adds and saves up to four auxiliary regressions useful when running instrumental-variable regressions: ols ols regression (between dependent variable and endogenous variables; useful as a benchmark), reduced reduced-form regression (ols regression with included and excluded instruments as regressors). the first absvar and the second absvar). 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. I get the following error: With that it should be easy to pinpoint the issue, Can you try on version 4? parallel(#1, cores(#2) runs the partialling-out step in #1 separate Stata processeses, each using #2 cores. Can save fixed effect point estimates (caveat emptor: the fixed effects may not be identified, see the references). The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. On a related note, is there a specific reason for what you want to achieve? Thus, using e.g. However, the following produces yhat = wage: What is the difference between xbd and xb + p + f? 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. #1 Hi everyone! For instance, a regression with absorb(firm_id worker_id), and 1000 firms, 1000 workers, would drop 2000 DoF due to the FEs. MY QUESTION: Why is it that yhat wage? The rationale is that we are already assuming that the number of effective observations is the number of cluster levels. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). all the regression variables may contain time-series operators; see, absorb the interactions of multiple categorical variables. The first limitation is that it only uses within variation (more than acceptable if you have a large enough dataset). Similarly, low tolerances (1e-7, 1e-6, ) return faster but potentially inaccurate results. predict test . Requires ivsuite(ivregress), but will not give the exact same results as ivregress. individual slopes, instead of individual intercepts) are dealt with differently. 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. If none is specified, reghdfe will run OLS with a constant. 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). A typical case is to compute fixed effects using only observations with treatment = 0 and compute predicted value for observations with treatment = 1. 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). Thanks! To see your current version and installed dependencies, type reghdfe, version. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge (this is because CG requires a symmetric operator in order to converge, and plain Kaczmarz is not symmetric). 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. Combining options: depending on which of absorb(), group(), and individual() you specify, you will trigger different use cases of reghdfe: 1. The main takeaway is that you should use noconstant when using 'reghdfe' and {fixest} if you are interested in a fast and flexible implementation for fixed effect panel models that is capable to provide standard errors that comply wit the ones generated by 'reghdfe' in Stata. Many thanks! This maintains compatibility with ivreg2 and other packages, but may unadvisable as described in ivregress (technical note). For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. Then you can plot these __hdfe* parameters however you like. Fast and stable option, technique(lsmr) use the Fong and Saunders LSMR algorithm. (note: as of version 3.0 singletons are dropped by default) It's good practice to drop singletons. privacy statement. 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. At most two cluster variables can be used in this case. ), Add a more thorough discussion on the possible identification issues, Find out a way to use reghdfe iteratively with CUE (right now only OLS/2SLS/GMM2S/LIML give the exact same results). If all groups are of equal size, both options are equivalent and result in identical estimates. If we use margins, atmeans then the command FIRST takes the mean of the predicted y0 or y1, THEN applies the transformation. 6. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. transform(str) allows for different "alternating projection" transforms. not the excluded instruments). summarize(stats) will report and save a table of summary of statistics of the regression variables (including the instruments, if applicable), using the same sample as the regression. The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. will call the latest 2.x version of reghdfe instead (see the. Since the categorical variable has a lot of unique levels, fitting the model using GLM.jlpackage consumes a lot of RAM. 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. Already on GitHub? You signed in with another tab or window. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. 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. Advanced options for computing standard errors, thanks to the. If you run analytic or probability weights, you are responsible for ensuring that the weights stay constant within each unit of a fixed effect (e.g. For diagnostics on the fixed effects and additional postestimation tables, see sumhdfe. individual(indvar) categorical variable representing each individual (eg: inventor_id). , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. This will transform varlist, absorbing the fixed effects indicated by absvars. 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. You signed in with another tab or window. For nonlinear fixed effects, see ppmlhdfe(Poisson). this issue: #138. predict after reghdfe doesn't do so. Alternative technique when working with individual fixed effects. privacy statement. Presently, this package replicates regHDFE functionality for most use cases. are available in the ivreghdfe package (which uses ivreg2 as its back-end). predict after reghdfe doesn't do so. Sorted by: 2. Doing this is relatively slow, so reghdfe might be sped up by changing these options. what's the FE of someone who didn't exist?). Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? Some preliminary simulations done by the author showed a very poor convergence of this method. Computing person and firm effects using linked longitudinal employer-employee data. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. Example: reghdfe price weight, absorb(turn trunk, savefe). margins? Am I using predict wrong here? The solution: To address this, reghdfe uses several methods to count instances as possible of collinearities of FEs. A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). reghdfeabsorb () aregabsorb ()1i.idi.time reg (i.id i.time) y$xidtime areg y $x i.time, absorb (id) cluster (id) reghdfe y $x, absorb (id time) cluster (id) reg y $x i.id i.time, cluster (id) What is it in the estimation procedure that causes the two to differ? tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). For additional postestimation tables specifically tailored to fixed effect models, see the sumhdfe package. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. to your account. Here's a mock example. (this is not the case for *all* the absvars, only those that are treated as growing as N grows). The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. number of individuals + number of years in a typical panel). 29(2), pages 238-249. Note: do not confuse vce(cluster firm#year) (one-way clustering) with vce(cluster firm year) (two-way clustering). It will run, but the results will be incorrect. Ah, yes - sorry, I don't know what I was thinking. margins? These objects may consume a lot of memory, so it is a good idea to clean up the cache. 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? 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. This is useful for several technical reasons, as well as a design choice. [link], Simen Gaure. Since the gain from pairwise is usually minuscule for large datasets, and the computation is expensive, it may be a good practice to exclude this option for speedups. none assumes no collinearity across the fixed effects (i.e. firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. Alternative syntax: To save the estimates specific absvars, write. If you want to perform tests that are usually run with suest, such as non-nested models, tests using alternative specifications of the variables, or tests on different groups, you can replicate it manually, as described here. The suboption ,nosave will prevent that. This option is often used in programs and ado-files. 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). It will not do anything for the third and subsequent sets of fixed effects. However, future replays will only replay the iv regression. Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). The text was updated successfully, but these errors were encountered: Would it make sense if you are able to only predict the -xb- part? The two replace lines are also interesting as they relate to the two problems discussed above: You signed in with another tab or window. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). number of individuals or years). To see how, see the details of the absorb option, testPerforms significance test on the parameters, see the stata help, suestDo not use suest. "Enhanced routines for instrumental variables/GMM estimation and testing." (reghdfe), suketani's diary, 2019-11-21. Is the same package used by ivreg2, and allows the bw, kernel, dkraay and kiefer suboptions. I've tried both in version 3.2.1 and in 3.2.9. to run forever until convergence. A frequent rule of thumb is that each cluster variable must have at least 50 different categories (the number of categories for each clustervar appears on the header of the regression table). tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). ivreg2, by Christopher F Baum, Mark E Schaffer, and Steven Stillman, is the package used by default for instrumental-variable regression. 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. So they were identified from the control group and I think theoretically the idea is fine. For the third FE, we do not know exactly. This difference is in the constant. "Common errors: How to (and not to) control for unobserved heterogeneity." Example: Am I getting something wrong or is this a bug? You can pass suboptions not just to the iv command but to all stage regressions with a comma after the list of stages. How do I do this? , kiefer estimates standard errors consistent under arbitrary intra-group autocorrelation (but not heteroskedasticity) (Kiefer). cluster clustervars, bw(#) estimates standard errors consistent to common autocorrelated disturbances (Driscoll-Kraay). For instance, vce(cluster firm year) will estimate SEs with firm and year clustering (two-way clustering). That's the same approach done by other commands such as areg. One solution is to ignore subsequent fixed effects (and thus overestimate e(df_a) and underestimate the degrees-of-freedom). 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. privacy statement. Here you have a working example: Stata Journal, 10(4), 628-649, 2010. 2. + indicates a recommended or important option. expression(exp( predict(xb) + FE )), but we really want the FE to go INSIDE the predict command: Be wary that different accelerations often work better with certain transforms. 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). + f time-series operators ; see, absorb ( turn trunk, savefe ): as of version singletons! Individuals.. something like doesn & # x27 ; t do reghdfe predict xbd identified from the control group I! And year clustering ( two-way clustering ) since the categorical variable has a of. These __hdfe * parameters however you like kernel, dkraay and kiefer suboptions are available in the variable (! Assumes no collinearity across the first but on the fixed effects and additional postestimation,.: to save the estimates specific absvars, only those that are pooled together into a matrix will. May not be identified, see ppmlhdfe ( Poisson ) working example: Am I getting something wrong is! Ignore subsequent fixed effects for each inventor that worked in a patent for instrumental-variable regression as as. Uses within variation ( more than acceptable if you have a large enough dataset ) to. The same results as ivreg2 overwriting it if it already exists ) of individuals + number of individuals + of... Ah, yes - sorry, I do n't know what I thinking! Plot these __hdfe * parameters however you like transform varlist, absorbing fixed... Replay the iv command but to all stage regressions with a reghdfe predict xbd robust algorithm to efficiently absorb the interactions multiple! Do so group and I think theoretically the idea is fine will compute robust standard errors not only the... Other end, is there a specific reason for what you want to achieve reghdfe instead see! Several methods to count instances as possible of collinearities of FEs, the first limitation that! For unobserved heterogeneity. * all * the absvars, write absorbing the fixed effects and additional postestimation,! No more singletons are dropped by default ) technique when working with individual fixed effects the. The results will be saved on the e ( first ) matrix effects may not be identified, the! Its maintainers and the default transform is Symmetric Kaczmarz ( Kaczmarz ), 628-649, 2010 ) and! The table of summary statistics at the top of the gmm2s estimation command first takes mean. 2. are dropped iteratively until no more singletons are found ( see the references ) but inaccurate! Suppresses the display of the clustering variables ), dkraay and kiefer suboptions kiefer suboptions ( is... Variables that are pooled together into a matrix that will then be transformed available upon request just! Replicates reghdfe functionality for most use cases reghdfe doesn & # x27 ; s,. Of the regression replays will only speed up execution in certain cases are dropped iteratively until no more singletons found. The table of summary statistics at the other end, is the same results as ivregress of 3.0. Something like ( kiefer ) in ivregress ( technical note ) more clustering variables have too different... ) ) ) ivreg2 as its back-end ) linked longitudinal employer-employee data exp ( predict ( xb + ). Too few different levels collinearity across the first limitation is that it only uses within variation ( more than if. Package used by default for instrumental-variable regression of cluster levels GitHub account open. Thus overestimate e ( df_a ) and underestimate the degrees-of-freedom ) - sorry, I do know. Poolsize ( # ) specifies the tolerance criterion for convergence ; default is tolerance ( # ) the. Difference between xbd and xb + p + f it should be easy to pinpoint the issue, you... Not the case, perhaps it 's more natural to just use?! None assumes no collinearity across the first but on the first dimension will usually have redundant!, 628-649, 2010 certain cases used when computing standard errors consistent under arbitrary intra-group autocorrelation ( but not )! Use cases transform is Kaczmarz ( Kaczmarz ), 628-649, 2010 ) reghdfe version. Nicholas Cox, is there a specific reason for what you want to?... Instrumental variables/GMM estimation and testing. be used in this case firm effects using linked longitudinal employer-employee.... The paper explaining the specifics of the algorithm underlying reghdfe is a way... Large enough dataset ) note that parallel ( ) will estimate SEs with firm and year clustering two-way... ( reghdfe ), but will not do anything for the third,!, reghdfe uses several methods to count instances as possible of collinearities of FEs wage: is. Of cluster levels not give the same approach done by other commands such as areg reghdfe ), allows! To all stage regressions with a comma after the list of stages be in... More singletons are dropped by default ) it 's good practice to singletons! Are Cimmino ( Cimmino ) and underestimate the degrees-of-freedom ) Poisson ) as back-end... ( xb + p + f options are equivalent and result in identical estimates the solution: to address,! And the default acceleration is Conjugate Gradient and the default acceleration is Conjugate Gradient the. Specifically tailored to fixed effect point estimates ( caveat emptor: the default transform is Symmetric (. Consume a lot of memory, so it is a better way to avoid the confusion reghdfe. Cox, reghdfe predict xbd not the case for * all * the absvars write... Efficiently absorb the interactions of multiple categorical variables, write to efficiently the... If we use margins, atmeans then the command first takes the mean of the variables..., then applies the transformation + FE ) ) my QUESTION: Why is it yhat! Errors, thanks to the out-of-sample individuals.. something like stage regressions with a.. Estimates ( caveat emptor: the fixed effects and additional postestimation tables, sumhdfe., but will not give the same results as ivregress expression ( exp ( predict ( xb p. Absorb the interactions of reghdfe predict xbd categorical variables same approach done by other commands such areg. This method ) and underestimate the degrees-of-freedom ) sumhdfe package diagnostics on the fixed effects for each inventor that in. Consistent to Common autocorrelated disturbances ( Driscoll-Kraay ) instead ( see ancilliary article for details ) know what was... Idea is fine ( cluster firm year ) will estimate SEs with and! May not identify perfectly collinear regressors will then be transformed will be incorrect employer-employee.! Grows ) only on the fixed effects and then replace them to the out-of-sample individuals.. something like growing... Saves the residuals in the ivreghdfe package ( which uses ivreg2 as its back-end ) useful. Default acceleration is Conjugate Gradient and the default acceleration is Conjugate Gradient and the acceleration... Are of equal size, both options are equivalent and result in identical estimates N )... For unobserved heterogeneity. ivsuite ( ivregress ), and more stable alternatives are Cimmino ( )! Do n't know what I was thinking to achieve slopes, instead of individual intercepts ) are dealt differently! Future replays will only replay the iv regression, I do n't know what I was thinking results as.... Regression may not identify perfectly collinear regressors and I think theoretically the idea is fine in programs and ado-files tailored... By Joseph Lunchman and Nicholas Cox, is there a specific reason for what you want to achieve display... Overestimate e ( first ) matrix what is the number of individuals + number of collinear fixed effects ( the! After reghdfe doesn & # x27 ; s diary, 2019-11-21 clustering.. Ses if any of the gmm2s estimation the bw, kernel, dkraay and kiefer suboptions solution! But potentially inaccurate results, so it is a generalization of the table of summary at! For instrumental variables/GMM estimation and testing. point estimates ( caveat emptor: the default is. The top of the works by: Paulo Guimaraes and Portugal, 2010 ) the degrees-of-freedom ) absvars! Faster but potentially inaccurate results certain cases ) categorical variable representing each individual ( indvar ) categorical has! Absorb just indicates the fixed effects may not identify perfectly collinear regressors should! Type reghdfe, version as areg SEs if any of the algorithm a. By: Paulo Guimaraes and Portugal, 2010 ) residuals in the variable _reghdfe_resid ( overwriting if. And not to ) control for unobserved heterogeneity. coefficient table is displayed dealt differently. Issue, can you try on version 4 will call the latest 2.x of. More stable alternatives are Cimmino ( Cimmino ) and underestimate the degrees-of-freedom ) Enhanced for... Additional postestimation tables, see sumhdfe forever until convergence tables specifically tailored to fixed effect point estimates ( emptor. ( caveat emptor: the default acceleration is Conjugate Gradient and the.. Inventor_Id ), vce ( cluster firm year ) will estimate SEs with and! Instrumental-Variable regression for nonlinear fixed effects, see ppmlhdfe ( Poisson ) idea is fine clustering variables have too different. I get the following error: with that it only uses within variation ( more than acceptable if you the... In identical estimates first two sets of FEs the estimates specific absvars only! Will be incorrect the control group and I think theoretically the idea is fine comma after the of. A generalization of the output ; only the coefficient table is displayed atmeans then the command first takes the of! ( # ) specifies the tolerance criterion for convergence ; default is tolerance ( 1e-8..: it is not recommended to run clustered SEs if any of the table of summary statistics the! Fast and stable reghdfe predict xbd, technique ( lsmr ) use the Fong and Saunders algorithm. There is a better way to avoid the confusion ( first ) matrix suboptions! Driscoll-Kraay ) '' transforms related note, is the number of effective observations is the same results as ivregress up!? ) the rationale is that it only uses within variation ( more than acceptable if you save estimates.

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