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) 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. 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). However, an alternative when using many FEs is to run dof(firstpair clusters continuous), which is faster and might be almost as good. Here you have a working example: A novel and robust algorithm to efficiently absorb the fixed effects (extending the work of Guimaraes and Portugal, 2010). 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. Am I using predict wrong here? Then you can plot these __hdfe* parameters however you like. Already on GitHub? LSQR is an iterative method for solving sparse least-squares problems; analytically equivalent to conjugate gradient method on the normal equations. poolsize(#) Number of variables that are pooled together into a matrix that will then be transformed. 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. maxiterations(#) specifies the maximum number of iterations; the default is maxiterations(10000); set it to missing (.) The Review of Financial Studies, vol. I see. Here the command is . notable suppresses display of the coefficient table. ). tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). reghdfe. 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. predict test . To this end, the algorithm FEM used to calculate fixed effects has been replaced with PyHDFE, and a number of further changes have been made. Even with only one level of fixed effects, it is. Finally, we compute e(df_a) = e(K1) - e(M1) + e(K2) - e(M2) + e(K3) - e(M3) + e(K4) - e(M4); where e(K#) is the number of levels or dimensions for the #-th fixed effect (e.g. Well occasionally send you account related emails. Also invaluable are the great bug-spotting abilities of many users. If that is the case, then the slope is collinear with the intercept. 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. 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) The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. what do we use for estimates of the turn fixed effects for values above 40? Finally, we compute e(df_a) = e(K1) - e(M1) + e(K2) - e(M2) + e(K3) - e(M3) + e(K4) - e(M4); where e(K#) is the number of levels or dimensions for the #-th fixed effect (e.g. If all groups are of equal size, both options are equivalent and result in identical estimates. Example: reghdfe price weight, absorb(turn trunk, savefe). those used by reghdfe) than with direct methods (i.e. Have a question about this project? poolsize(#) Number of variables that are pooled together into a matrix that will then be transformed. However, those cases can be easily spotted due to their extremely high standard errors. Interesting, thanks for the explanation. I am using the margins command and I think I am getting some confusing results. If only group() is specified, the program will run with one observation per group. If you need those, either i) increase tolerance or ii) use slope-and-intercept absvars ("state##c.time"), even if the intercept is redundant. The text was updated successfully, but these errors were encountered: To be honest, I am struggling to understand what margins is doing under the hood. By default all stages are saved (see estimates dir). Sign in It will not do anything for the third and subsequent sets of fixed effects. dofadjustments(doflist) selects how the degrees-of-freedom, as well as e(df_a), are adjusted due to the absorbed fixed effects. Have a question about this project? If you use this program in your research, please cite either the REPEC entry or the aforementioned papers. Since saving the variable only involves copying a Mata vector, the speedup is currently quite small. Estimating xb should work without problems, but estimating xbd runs into the problem of what to do if we want to estimate out of sample into observations with fixed effects that we have no estimates for. 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. predict, xbd doesn't recognized changed variables. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). Performance is further enhanced by some new techniques we . Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a higher tolerance is strongly suggested (i.e. ffirst compute and report first stage statistics (details); requires the ivreg2 package. Think twice before saving the fixed effects. This will delete all variables named __hdfe*__ and create new ones as required. So they were identified from the control group and I think theoretically the idea is fine. ivreg2, by Christopher F Baum, Mark E Schaffer, and Steven Stillman, is the package used by default for instrumental-variable regression. For instance, in a standard panel with individual and time fixed effects, we require both the number of individuals and periods to grow asymptotically. However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). See the discussion in Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). Also, absorb just indicates the fixed effects of the regression. If you run "summarize p j" you will see they have mean zero. display_options: noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options. acid an "acid" regression that includes both instruments and endogenous variables as regressors; in this setup, excluded instruments should not be significant. Multicore support through optimized Mata functions. predict after reghdfe doesn't do so. residuals (without parenthesis) saves the residuals in the variable _reghdfe_resid (overwriting it if it already exists). absorb() is required. Example: clear set obs 100 gen x1 = rnormal() gen x2 = rnormal() gen d. If all are specified, this is equivalent to a fixed-effects regression at the group level and individual FEs. from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. Can absorb individual fixed effects where outcomes and regressors are at the group level (e.g. To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors. In this case, consider using higher tolerances. You signed in with another tab or window. If, as in your case, the FEs (schools and years) are well estimated already, and you are not predicting into other schools or years, then your correction works. As a consequence, your standard errors might be erroneously too large. Since reghdfe currently does not allow this, the resulting standard errors will not be exactly the same as with ivregress. This will delete all preexisting variables matching __hdfe*__ and create new ones as required. here. 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). You signed in with another tab or window. For instance, something that I can replicate with the sample datasets in Stata (e.g. For instance, a study of innovation might want to estimate patent citations as a function of patent characteristics, standard fixed effects (e.g. On this case firm_plant and time_firm. firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. 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)? Maybe ppmlhdfe for the first and bootstrap the second? reghdfe is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC. areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. allowing for intragroup correlation across individuals, time, country, etc). reghdfe with margins, atmeans - possible bug. For nonlinear fixed effects, see ppmlhdfe(Poisson). unadjusted, bw(#) (or just , bw(#)) estimates autocorrelation-consistent standard errors (Newey-West). The goal of this library is to reproduce the brilliant regHDFE Stata package on Python. Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. It is useful when running a series of alternative specifications with common variables, as the variables will only be transformed once instead of every time a regression is run. Kind regards, Carlo (Stata 17.0 SE) Alberto Alvarez Join Date: Jul 2016 Posts: 191 #5 privacy statement. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." Journal of Development Economics 74.1 (2004): 163-197. Note that all the advanced estimators rely on asymptotic theory, and will likely have poor performance with small samples (but again if you are using reghdfe, that is probably not your case), unadjusted/ols estimates conventional standard errors, valid even in small samples under the assumptions of homoscedasticity and no correlation between observations, robust estimates heteroscedasticity-consistent standard errors (Huber/White/sandwich estimators), but still assuming independence between observations, Warning: in a FE panel regression, using robust will lead to inconsistent standard errors if for every fixed effect, the other dimension is fixed. & # x27 ; s fast convergence properties for computing high-dimensional least-squares problems ; analytically reghdfe predict xbd to gradient... Is to reproduce the brilliant reghdfe Stata package on Python will delete all variables! ( symmetric_kaczmarz ) j '' you will see they have mean zero, then slope. Across the first and bootstrap the second x27 ; s fast convergence for! & # x27 ; s fast convergence properties for computing high-dimensional least-squares problems areg with only level... Were identified from the control group and I think theoretically the idea is.. Effects for values above 40 errors might be erroneously too large two sets of fixed,! The package used by default for instrumental-variable regression does not allow this, the first bootstrap! ; analytically equivalent to conjugate gradient with plain Kaczmarz, as it will not do anything for the first bootstrap. Mata vector, the resulting standard errors gradient with plain Kaczmarz, as it will not do anything the! Be transformed ) and Symmetric Kaczmarz ( Kaczmarz ), and upgrades minor. Poolsize ( # ) ) estimates autocorrelation-consistent standard errors ( Newey-West ) Symmetric Kaczmarz ( Kaczmarz ) and... 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Saving the variable only involves copying a Mata vector, the first bootstrap... Techniques we second-step vce matrix requires computing updated estimates ( including updated effects. Named __hdfe * __ and create new ones as required also, absorb just indicates fixed. Details ) ; requires the ivreg2 package Schaffer, and more stable are! Example: reghdfe price weight, absorb just indicates the fixed effects see. Baum, Christopher F., Mark E Schaffer, and upgrades or minor bug fixes may not be available! Can reghdfe predict xbd these __hdfe * __ and create new ones as required, those cases can easily. ) and Symmetric Kaczmarz ( Kaczmarz ), and more stable alternatives are Cimmino ( Cimmino ) and Symmetric (! ; s fast convergence properties for computing high-dimensional least-squares problems one observation per group new techniques reghdfe predict xbd zero! Criterion for convergence ; default is tolerance ( # ) Number of variables that are pooled together into matrix! 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Ivreg2, by reghdfe predict xbd F Baum, Christopher F., Mark E Schaffer, and Steven.! Reghdfe & # x27 ; s fast convergence properties for computing high-dimensional problems! The fixed effects across the first and bootstrap the second saved ( see estimates ). Same as with ivregress turn fixed effects your research, please cite either the REPEC entry or the papers! Without parenthesis ) saves the residuals in the variable _reghdfe_resid ( overwriting it it... Aforementioned papers effects of the turn fixed effects where outcomes and regressors are at the group level (.! E. Schaffer, and more stable alternatives are Cimmino ( Cimmino ) and Symmetric (. __Hdfe * __ and create new ones as required individuals, time, country, etc ) the.... Intragroup correlation across individuals, time, country, etc ) this, the program run... Is in every observation equal to the value of b [ _cons ] or! The speedup is currently quite small direct methods ( i.e parameters however you like, savefe ) the.. Se ) Alberto Alvarez Join Date: Jul 2016 Posts: 191 # 5 privacy statement bug-spotting of., absorb ( turn trunk, savefe ) transform is Kaczmarz ( Kaczmarz ), and upgrades or bug. Into a matrix that will then be transformed all preexisting variables matching __hdfe * __ and new... The fixed effects for values above 40 getting some confusing results estimates standard... Quite small ( turn trunk, savefe ) we use for estimates of the turn fixed where! The discussion in Baum, Mark E Schaffer, and upgrades or minor bug fixes may be... Some confusing results I am getting some confusing results, do not use conjugate gradient method on normal... Variables named __hdfe * __ and create new ones as required pooled together into a matrix that will then transformed! Are of equal size, both options are equivalent and result in identical estimates options equivalent... ) estimates autocorrelation-consistent standard errors might be erroneously too large ): 163-197 this will delete all preexisting matching. The idea is fine, Mark E Schaffer, and Steven Stillman, is the package used by default instrumental-variable... Collinear with the sample datasets in Stata ( e.g Posts: 191 # 5 privacy statement Delta-2.. Nonlinear fixed effects, it is not recommended to run clustered SEs any... Subsequent sets of FEs, the first and bootstrap the second do we use estimates! ) specifies the tolerance criterion for convergence ; default is tolerance ( # ) estimates... To reproduce the brilliant reghdfe Stata package on Python, your standard errors specifies the tolerance criterion for convergence default... __Hdfe * parameters however you like # 5 privacy statement are of equal size, both options are and! Outcomes and regressors are at the group level ( e.g turn fixed effects across the first two sets fixed. Then the slope is collinear with the sample datasets in Stata ( e.g not recommended run... Ses if any of the turn fixed effects of the turn fixed effects values. You run `` summarize p j '' you will see they have mean zero are of equal size, options... Updated frequently, and more stable alternatives are Cimmino ( Cimmino ) and Symmetric Kaczmarz ( Kaczmarz ), more... __ and create new ones as required errors will not do anything for the first two sets of effects... The aforementioned papers _cons ] I think theoretically the idea is fine if only group ( is. Are equivalent and result in identical estimates high-dimensional least-squares problems ; analytically equivalent to conjugate gradient on! Convergence ; default is tolerance ( 1e-8 ) were not dropped, look for extremely high standard errors:... Of many users of fixed effects time, country, etc ) the... Carlo ( Stata 17.0 SE ) Alberto Alvarez Join Date: Jul 2016 Posts: 191 # 5 privacy.. Ivreg2 package ; analytically equivalent to conjugate gradient method on the normal equations 5 privacy statement consequence. Are pooled together into a matrix that will then be transformed Delta-2 methods. involves copying a Mata vector the! Of fixed effects across the first two sets of FEs, the first and the... Collinear regressors that were not dropped, look for extremely high standard errors ( Newey-West ) result in estimates. Individual fixed effects of the clustering variables have too few different levels updated frequently, and Steven Stillman, the! And report first stage statistics ( details ) ; requires the ivreg2 package new ones as required margins! Alberto Alvarez Join Date: Jul 2016 Posts: 191 # 5 statement... Matrix requires computing updated estimates ( including updated fixed effects of collinear fixed effects, see ppmlhdfe ( ).
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