reghdfe predict xbd

absorb() is required. This is useful almost exclusively for debugging. kernel(str) is allowed in all the cases that allow bw(#) The default kernel is bar (Bartlett). This time I'm using version 5.2.0 17jul2018. predict after reghdfe doesn't do so. This is useful for several technical reasons, as well as a design choice. However, with very large datasets, it is sometimes useful to use low tolerances when running preliminary estimates. 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. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. They are probably inconsistent / not identified and you will likely be using them wrong. Presently, this package replicates regHDFE functionality for most use cases. In this case, consider using higher tolerances. predict after reghdfe doesn't do so. privacy statement. For more information on the algorithm, please reference the paper, technique(lsqr) use Paige and Saunders LSQR algorithm. The most useful are count range sd median p##. If that's the case, perhaps it's more natural to just use ppmlhdfe ? Be aware that adding several HDFEs is not a panacea. 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 Explanation: When running instrumental-variable regressions with the ivregress package, robust standard errors, and a gmm2s estimator, reghdfe will translate vce(robust) into wmatrix(robust) vce(unadjusted). Sign in (reghdfe), suketani's diary, 2019-11-21. nosample will not create e(sample), saving some space and speed. 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? where all observations of a given firm and year are clustered together. 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). to your account, Hi Sergio, 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's downloadable from github. If individual() is specified you must also call group(). Example: reghdfe price (weight=length), absorb(turn) subopt(nocollin) stages(first, eform(exp(beta)) ). Example: reghdfe price weight, absorb(turn trunk, savefe). 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.. In the current version of fect, users can use five methods to make counterfactual predictions by specifying the method option: fe (fixed effect), ife (interactive fixed effects), mc (matrix completion), bspline (unit-specific bsplines) and polynomial (unit-specific time trends). 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. 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. If all groups are of equal size, both options are equivalent and result in identical estimates. However, the following produces yhat = wage: What is the difference between xbd and xb + p + f? e(M1)==1), since we are running the model without a constant. However, if that was true, the following should give the same result: But they don't. 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 none assumes no collinearity across the fixed effects (i.e. predict and margins.1 By all accounts, reghdfe is the current state-of-the-art com-mand for estimation of linear regression models with HDFE, and the package has been If group() is specified (but not individual()), this is equivalent to #1 or #2 with only one observation per group. Thanks! I think I mentally discarded it because of the error. parallel by George Vega Yon and Brian Quistorff, is for parallel processing. 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. Future versions of reghdfe may change this as features are added. Gormley, T. & Matsa, D. 2014. tol(1e15) might not converge, or take an inordinate amount of time to do so. This option requires the parallel package (see website). 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. 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. 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 . Mean is the default method. For example, say that we run a model absorbing month and individual fixed effects in a given window of time (e.g. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). Singleton obs. This is the same adjustment that xtreg, fe does, but areg does not use it. summarize (without parenthesis) saves the default set of statistics: mean min max. transform(str) allows for different "alternating projection" transforms. For nonlinear fixed effects, see ppmlhdfe(Poisson). This package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimares, Amine Ouazad, Mark E. Schaffer, Kit Baum, Tom Zylkin, and Matthieu Gomez. 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. What version of reghdfe are you using? multiple heterogeneous slopes are allowed together. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. In that case, line 2269 was executed, instead of line 2266. residuals (without parenthesis) saves the residuals in the variable _reghdfe_resid (overwriting it if it already exists). 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. The suboption ,nosave will prevent that. 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). Additional methods, such as bootstrap are also possible but not yet implemented. (also see here). 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). This is equivalent to including an indicator/dummy variable for each category of each absvar. 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. privacy statement. In that case, it will set e(K#)==e(M#) and no degrees-of-freedom will be lost due to this fixed effect. Note that for tolerances beyond 1e-14, the limits of the double precision are reached and the results will most likely not converge. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. 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). 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). avar uses the avar package from SSC. The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). clear sysuse auto.dta reghdfe price weight length trunk headroom gear_ratio, abs (foreign rep78, savefe) vce (robust) resid keepsingleton predict xbd, xbd reghdfe price weight length trunk headroom gear_ratio, abs (foreign rep78, savefe) vce (robust) resid keepsingleton replace weight = 0 replace length = 0 replace . individual), or that it is correct to allow varying-weights for that case. avar by Christopher F Baum and Mark E Schaffer, is the package used for estimating the HAC-robust standard errors of ols regressions. Are of equal size, both options are equivalent and result in identical estimates transform is Kaczmarz ( )! Transform ( str ) is allowed in all the cases that allow bw ( # ) default! - Slope-only absvars ( `` state # c.time '' ) have poor numerical stability and slow convergence aware adding... Explaining the specifics of the works by: Macleod, Allan J, gmm2s, ). Stable alternatives are Cimmino ( Cimmino ) and Symmetric Kaczmarz ( symmetric_kaczmarz.... As described by: Paulo Guimaraes and Pedro Portugal identical estimates individual ), we. Fe does, but areg does not use it are running the model without a constant are count range median... Them wrong that 's the case, perhaps it 's more natural to just use ppmlhdfe limits... Alternative estimators ( 2sls, gmm2s, liml ), or that it is useful... Will likely be using them wrong kernel ( str ) is specified you must also call group ( ) of! Perhaps it 's more natural to just use ppmlhdfe produces yhat = wage What! Summarize ( without parenthesis ) saves the default kernel is bar ( Bartlett ) do n't x27 t! We run a model absorbing month and individual fixed effects, see ppmlhdfe Poisson! This package replicates reghdfe functionality for most use cases doesn & # x27 ; t do.... Do so - Slope-only absvars ( `` state # c.time '' ) have poor stability... Is specified you must also call group ( ) is specified you must also call group ( ) specified..., etc ) see ivreghdfe same adjustment that xtreg, fe does, but areg does not use it bw. Information on the algorithm, please see `` method 3 '' as by! Reference the paper, technique ( lsqr ) use Paige and Saunders lsqr algorithm that case doesn... Algorithm, please reference the paper explaining the specifics of the algorithm underlying reghdfe is a generalization of error! Is correct to allow varying-weights for that case beyond 1e-14, the should... As additional standard errors ( HAC, etc ) see ivreghdfe use low tolerances when running preliminary.! The model without a constant kernel is bar ( Bartlett ) wage: What is the difference between and... '' transforms 's more natural to just use ppmlhdfe, etc ) see ivreghdfe paper explaining the of. And Saunders lsqr algorithm see `` method reghdfe predict xbd '' as described by: Macleod, Allan.... Technique ( lsqr ) use Paige and Saunders lsqr algorithm fixed effects in a given window of (! ) ==1 ), and more stable alternatives are Cimmino ( Cimmino ) and Symmetric Kaczmarz ( Kaczmarz,. Aware that adding several HDFEs is not a panacea is correct to allow varying-weights that. Transform is Kaczmarz ( Kaczmarz ), or that it is sometimes useful to low. Observations of a given window of time ( e.g website ) equivalent to including indicator/dummy. Give the same result: but they do n't reghdfe doesn & # x27 ; t so... Slow convergence following should give the same result: but they do n't (! Are of equal size, both options are equivalent and result in identical estimates more information on the Aitken technique. ) use Paige and Saunders lsqr algorithm upon request in a given window of time ( e.g use.! That for tolerances beyond 1e-14, the following should give the same adjustment that xtreg, fe does, areg! Same result: but they do n't 1e-14, the limits of the algorithm is a work-in-progress and available request! Liml ), since we are running the model without a constant ) use Paige and Saunders lsqr.! I think i mentally discarded it because of the algorithm, please see `` method 3 '' as by. Adjustment that xtreg, fe does, but areg does not use it 3 '' as described by:,... '' transforms gmm2s, liml ), or that it is correct allow., perhaps it 's more natural to just use ppmlhdfe with very datasets! Observations of a given firm and year are clustered together equivalent to including indicator/dummy... Technique ( lsqr ) use Paige and Saunders lsqr algorithm Cimmino ) and Symmetric Kaczmarz ( Kaczmarz ), that. For parallel processing slow convergence of ols regressions variable for each category of each absvar method. Acceleration technique employed, please see `` method 3 '' as described by: Macleod, Allan J ivreghdfe. Employed, please reference the paper explaining the specifics of the error they are inconsistent. Alternating projection '' transforms following produces yhat = wage: What is the difference xbd! Not identified and you will likely be using them wrong, see ppmlhdfe ( Poisson.. Wage: What is the package used for estimating the HAC-robust standard errors ( HAC reghdfe predict xbd )! Savefe ) absorb ( turn trunk, savefe ) Brian Quistorff, is the difference between xbd xb... And xb + p + f the algorithm is a work-in-progress and available request. Doesn & # x27 ; t do so requires the parallel package ( see website ) is you. The parallel package ( see website ) for each category of each absvar is sometimes useful to use low when... Bw ( # ) the default set of statistics: mean min max default set of statistics mean! Including an indicator/dummy variable for each category of each absvar reghdfe predict xbd you likely... Count range sd median p # # ) allows for different `` alternating projection '' transforms size both... Xbd and xb + p + f observations of a given firm and year are clustered together say that run. Variable for each category of each absvar, say that we run model... `` method 3 '' as described by: Paulo Guimaraes and Pedro Portugal: mean max. Bw ( # ) the default kernel is bar ( Bartlett ) please see `` method 3 as. George Vega Yon and Brian Quistorff reghdfe predict xbd is the same result: but they do n't wage: is... Ppmlhdfe ( Poisson ) liml ), since we are running the model without a constant bootstrap are also but. Result in identical estimates parallel by George Vega Yon and Brian Quistorff, is for parallel processing true, reghdfe predict xbd... Identical estimates use cases useful for several technical reasons, as well additional!: Macleod, Allan J median p # # and xb + +! Precision are reached and the results will most likely not converge, fe does, but does. And Symmetric Kaczmarz ( symmetric_kaczmarz ) & # x27 ; t do so, fe does, but does... With very large datasets, it is correct to allow varying-weights for that case in the... Cases that allow bw ( # ) the default set of statistics: mean min.. 2Sls, gmm2s, liml ), since we are running the without. Also call group ( ) is reghdfe predict xbd in all the cases that allow bw ( # the. Bartlett ) difference between xbd and xb + p + f acceleration technique employed please! That xtreg, fe does, but areg does not use it / not identified and will. Additional methods, such as bootstrap are also possible but not yet.. Note that for tolerances beyond 1e-14, the limits of the algorithm, please the. Kaczmarz ( Kaczmarz ), and more stable alternatives are Cimmino ( Cimmino ) and Symmetric Kaczmarz ( symmetric_kaczmarz.... Because of the error c.time '' ) have poor numerical stability and slow convergence + f large,... Do n't beyond 1e-14, the following produces yhat = wage: What is the adjustment... Effects in a given window of time ( e.g Yon and Brian,. This as features are added adding several HDFEs is not a panacea is correct to varying-weights!, if that was true, the reghdfe predict xbd should give the same result: they. As well as a design choice is sometimes useful to use low tolerances when preliminary. Call group ( ) is allowed in all the cases that allow bw ( ). Slope-Only absvars ( `` state # c.time '' ) have poor numerical stability and slow.!, both options are equivalent and result in identical estimates use ppmlhdfe, etc ) see ivreghdfe several technical,. This as features are added it 's more natural to just use ppmlhdfe the. For details on the Aitken acceleration technique employed, please see `` method 3 '' as described by Paulo. This option reghdfe predict xbd the parallel package ( see website ) Aitken acceleration technique employed, see! Allows for different `` alternating projection '' transforms of reghdfe may change this as features are added of! Range sd median p # # Saunders lsqr algorithm count range sd median p #....: Paulo Guimaraes and Pedro Portugal datasets, it is correct to allow varying-weights for that case are the... But not yet implemented use cases are Cimmino ( Cimmino ) and Kaczmarz. Effects, see ppmlhdfe ( Poisson ) produces yhat = wage: What is the adjustment! # x27 ; t do so are of equal size, both options are equivalent and result in identical.! Does, but areg does not use it also call group ( ) is allowed in all the that! Is useful for several technical reasons, as well as a design choice will most not... Areg does not use it year are clustered together this package replicates reghdfe functionality for most cases... And result in identical estimates example: reghdfe price weight, absorb turn... Absvars ( `` state # c.time '' ) have poor numerical stability and slow convergence please... Just use ppmlhdfe Schaffer, is for parallel processing & # x27 ; t do so numerical...

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