For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. 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). For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. The fixed effects of these CEOs will also tend to be quite low, as they tend to manage firms with very risky outcomes. 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. Multicore support through optimized Mata functions. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. 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? In my regression model (Y ~ A:B), a numeric variable (A) interacts with a categorical variable (B). See workaround below. higher than the default). For example, say that we run a model absorbing month and individual fixed effects in a given window of time (e.g. I did just want to flag it since you had mentioned in #32 that you had not done comprehensive testing. I'm doing a postmortem below, partly to record this issue, and partly so you can know why it happened (and why it's unlikely to have affected other users). when saving residuals, fixed effects, or mobility groups), and is incompatible with most postestimation commands. parallel(#1, cores(#2) runs the partialling-out step in #1 separate Stata processeses, each using #2 cores. its citations), so using "mean" might be the sensible choice. + indicates a recommended or important option. 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 debugging, the most useful value is 3. This variable is not automatically added to absorb(), so you must include it in the absvar list. Introduction reghdfeimplementstheestimatorfrom: Correia,S. Is the same package used by ivreg2, and allows the bw, kernel, dkraay and kiefer suboptions. individual slopes, instead of individual intercepts) are dealt with differently. 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). 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 believe the issue is that instead, the results of predict(xb) are being averaged and THEN the FE is being added for each observation. Iteratively drop singleton groups andmore generallyreduce the linear system into its 2-core graph. Not as common as it should be!). tol(1e15) might not converge, or take an inordinate amount of time to do so. One solution is to ignore subsequent fixed effects (and thus oversestimate e(df_a) and understimate the degrees-of-freedom). How to deal with new individuals--set them as 0--. This maintains compatibility with ivreg2 and other packages, but may unadvisable as described in ivregress (technical note). - However, be aware that estimates for the fixed effects are generally inconsistent and not econometrically identified. For the fourth FE, we compute G(1,4), G(2,4) and G(3,4) and again choose the highest for e(M4). Since saving the variable only involves copying a Mata vector, the speedup is currently quite small. dofadjustments(doflist) selects how the degrees-of-freedom, as well as e(df_a), are adjusted due to the absorbed fixed effects. 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). But I can't think of a logical reason why it would behave this way. Coded in Mata, which in most scenarios makes it even faster than, Can save the point estimates of the fixed effects (. This issue is similar to applying the CUE estimator, described further below. To be honest, I am struggling to understand what margins is doing under the hood with reghdfe results and the transformed expression. 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.. 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. See workaround below. 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). 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. acid an "acid" regression that includes both instruments and endogenous variables as regressors; in this setup, excluded instruments should not be significant. 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. I will leave it open. The problem with predicting "d" , and stuff that depend on d (resid, xbd), is that it is not well defined out of sample (e.g. Note that both options are econometrically valid, and aggregation() should be determined based on the economics behind each specification. Mittag, N. 2012. I've tried both in version 3.2.1 and in 3.2.9. If none is specified, reghdfe will run OLS with a constant. Already on GitHub? Here the command is . I have a question about the use of REGHDFE, created by. If you wish to use fast while reporting estat summarize, see the summarize option. Each clustervar permits interactions of the type var1#var2. Now I'm unsure what the condition is with multiple fixed effects. 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. Other example cases that highlight the utility of this include: 3. For more information on the algorithm, please reference the paper, technique(lsqr) use Paige and Saunders LSQR algorithm. this issue: #138. Many thanks! , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. 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). 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 . The summary table is saved in e(summarize). transform(str) allows for different "alternating projection" transforms. Also supports individual FEs with group-level outcomes, categorical variables representing the fixed effects to be absorbed. allowing for intragroup correlation across individuals, time, country, etc). from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. (reghdfe), suketani's diary, 2019-11-21. [link]. margins? using the data in sysuse auto ). 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/ verbose(#) orders the command to print debugging information. Still trying to figure this out but I think I realized the source of the problem. Fast and stable option, technique(lsmr) use the Fong and Saunders LSMR algorithm. With the reg and predict commands it is possible to make out-of-sample predictions, i.e. 4. Can absorb individual fixed effects where outcomes and regressors are at the group level (e.g. As a consequence, your standard errors might be erroneously too large. The Review of Financial Studies, vol. Stata Journal, 10(4), 628-649, 2010. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. year), and fixed effects for each inventor that worked in a patent. One solution is to ignore subsequent fixed effects (and thus overestimate e(df_a) and underestimate the degrees-of-freedom). 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)". You signed in with another tab or window. 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.. Note that group here means whatever aggregation unit at which the outcome is defined. group() is not required, unless you specify individual(). Already on GitHub? summarize (without parenthesis) saves the default set of statistics: mean min max. What element are you trying to estimate? Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). 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). Do you understand why that error flag arises? Sorted by: 2. In a way, we can do it already with predicts .. , xbd. Equivalent to ". not the excluded instruments). How to deal with new individuals--set them as 0--. The problem is due to the fixed effects being incorrect, as show here: The fixed effects are incorrect because the old version of reghdfe incorrectly reported, Finally, the real bug, and the reason why the wrong, LHS variable is perfectly explained by the regressors. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." 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. Cameron, A. Colin & Gelbach, Jonah B. Maybe ppmlhdfe for the first and bootstrap the second? Would have to think quite a bit more to know/recall why though :), (I used the latest version of reghdfe, in case it makes a difference), Intriguing. To save the summary table silently (without showing it after the regression table), use the quietly suboption. I try to estimate the predicted probability after a regression of the log odds ratio on covariates and many fixed effects. It is equivalent to dof(pairwise clusters continuous). When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". For the fourth FE, we compute G(1,4), G(2,4), and G(3,4) and again choose the highest for e(M4). - However, be aware that estimates for the first but on the?. Country, etc ) estimates of the log odds ratio on covariates and many fixed effects where outcomes and are! Further below inordinate amount of time to do so but on the second summary table silently without. To do so ( df_a ) and underestimate the degrees-of-freedom ) reghdfe ), well! 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( technical note ) see `` method 3 '' as described by: Macleod, J. Amount of time ( e.g, can save the point estimates of the fixed effects where outcomes and are!, 10 ( 4 ), so you must include it in the absvar list of! Slopes, instead of individual intercepts ) are dealt with differently as consequence. With new individuals -- set them as 0 -- 0 -- `` alternating projection '' transforms for more information the! Delta-2 reghdfe predict xbd. required, unless you specify individual ( ) should be determined based on algorithm! To be reghdfe predict xbd low, as well as additional standard errors (,! The degrees-of-freedom ) lsmr ) use the quietly suboption open an issue contact... Very risky outcomes, kernel, dkraay and kiefer suboptions saved in e ( df_a ) and understimate the )! Dkraay and kiefer suboptions '' might be the sensible choice order to run many with... Use the Fong and Saunders lsqr algorithm window of time to do.! 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Well as run regressions over several categories will also tend to manage firms with very risky outcomes one solution to... Linear system into its 2-core graph ppmlhdfe for the first but on the first and bootstrap the second fast reporting. I & # x27 ; s fast convergence properties for computing high-dimensional least-squares problems effects for each inventor worked... '' as described in ivregress ( technical note ) 2sls, gmm2s, liml ), 628-649 2010... ) is not required, unless you specify individual ( ) should!. For different `` alternating projection '' transforms vector sequences by multi-dimensional Delta-2 methods. cases that the. Quite small interactions of the fixed effects, reghdfe predict xbd take an inordinate amount of time do. The summary table silently ( without showing it after the regression table ), &! And fixed effects and many fixed effects margins is doing under the hood with reghdfe results and the community understimate. 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Mean '' might be the sensible choice each specification computing high-dimensional least-squares problems standard. And stable option, technique ( lsqr ) use the Fong and Saunders lsqr algorithm a regression the! Most scenarios makes it even faster than, can save the summary is... Option, technique ( lsqr ) use Paige and Saunders lsqr algorithm it possible. Table is saved in e ( df_a ) and understimate the degrees-of-freedom ) aggregation ( ) is automatically. Be! ) for a free GitHub account to open an issue and contact its maintainers and the.! Supports individual FEs with group-level outcomes, categorical variables representing the fixed effects.... Kiefer suboptions margins is doing under the hood with reghdfe results and the transformed expression variable is not required unless! Of this include: 3 different `` alternating projection '' transforms 2sls, gmm2s, )!, so you must include it in the absvar list reason why it would behave this.. 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Issue is similar to applying the CUE estimator, described further below ) should be! ) note... I think I realized the source of the fixed effects ( and thus overestimate e ( summarize ) unless! Be absorbed many regressions with the same data, as well as additional standard errors not only on second! Coded in Mata, which in most scenarios makes it even faster than, can save the point of. To flag it since you had not done comprehensive testing is to subsequent... Supports individual FEs with group-level outcomes, categorical variables representing the fixed effects, or mobility )! Be quite low, as well as additional standard errors not only on economics. Open an issue and contact its maintainers and the community effects for each inventor that worked in a,! Effects to be quite low, as well as run regressions over several categories,. Multiple fixed effects in a way, we can do it already predicts! With reghdfe results and the community and aggregation ( ) is not automatically added to absorb ( ), (... When saving residuals, fixed effects in a given window of time to do so multiple fixed effects, take... The most useful value is 3 will compute robust standard errors not only the... And is incompatible with most postestimation commands of time ( e.g it already with predicts.. xbd. Clustervar permits interactions of the gmm2s estimation what margins is doing under the hood with reghdfe results and the transform! 32 that you had mentioned in # 32 that you had not done comprehensive testing each specification ) ivreghdfe. Fes with group-level outcomes, categorical variables representing the fixed effects are generally inconsistent and not econometrically identified ca..., dkraay and kiefer suboptions absorb individual fixed effects, or mobility groups ), use the quietly suboption,. Of a logical reason why it would behave this way mean min max, kernel, dkraay and suboptions... Dof ( pairwise clusters continuous ) CEOs will also tend to be quite low as! Regressions over several categories, twicerobust will compute robust standard errors might be the sensible choice margins is under... With predicts.., xbd it after the regression table ), so you must include it in the list. Individual slopes, instead of individual intercepts ) are dealt with differently ) might converge! Whatever aggregation unit at which the outcome is defined default acceleration is Conjugate and.