This package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum. If group() is specified (but not individual()), this is equivalent to #1 or #2 with only one observation per group. The Review of Financial Studies, vol. Estimate on one dataset & predict on another. Both the absorb() and vce() options must be the same as when the cache was created (the latter because the degrees of freedom were computed at that point). Multi-way-clustering is allowed. predict after reghdfe doesn't do so. Well occasionally send you account related emails. If only absorb() is present, reghdfe will run a standard fixed-effects regression. I am running the following commands: Code: reghdfe log_odds_ratio depvar [pw=weights], absorb (year county_fe) cluster (state) resid predictnl pred_prob=exp (predict (xbd))/ (1+exp (predict (xbd))) , se (pred_prob_se) Note that fast will be disabled when adding variables to the dataset (i.e. It will run, but the results will be incorrect. program define reghdfe_old_p * (Maybe refactor using _pred_se ??) Sign up for a free GitHub account to open an issue and contact its maintainers and the community. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. 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. 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. Alternative syntax: To save the estimates specific absvars, write. 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.. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. You can check that easily when running e.g. noheader suppresses the display of the table of summary statistics at the top of the output; only the coefficient table is displayed. Requires pairwise, firstpair, or the default all. Sign in Stata: MP 15.1 for Unix. Calculating the predictions/average marginal effects is OK but it's the confidence intervals that are giving me trouble. one- and two-way fixed effects), but in others it will only provide a conservative estimate. reghdfe now permits estimations that include individual fixed effects with group-level outcomes. Warning: cue will not give the same results as ivreg2. areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. For debugging, the most useful value is 3. "New methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from Germany." transform(str) allows for different "alternating projection" transforms. 2023-4-08 | 20237. 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: acceleration(str) Relevant for tech(map). This variable is not automatically added to absorb(), so you must include it in the absvar list. Many thanks! For more information on the algorithm, please reference the paper, technique(lsqr) use Paige and Saunders LSQR algorithm. Alternative technique when working with individual fixed effects. Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. To follow, you need the latest versions of reghdfe and ftools (from github): In this line, we run Stata's test to get e(df_m). The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. 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. See the discussion in Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. For debugging, the most useful value is 3. For the fourth FE, we compute G(1,4), G(2,4) and G(3,4) and again choose the highest for e(M4). Is there an option in predict to compute predicted value outside e(sample), as in reg? Thus, using e.g. This is potentially too aggressive, as many of these fixed effects might be perfectly collinear with each other, and the true number of DoF lost might be lower. groupvar(newvar) name of the new variable that will contain the first mobility group. However, the following produces yhat = wage: What is the difference between xbd and xb + p + f? 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. Login or. You can use it by itself (summarize(,quietly)) or with custom statistics (summarize(mean, quietly)). In contrast, other production functions might scale linearly in which case "sum" might be the correct choice. 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). It will run, but the results will be incorrect. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. 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. The community-contributed module -reghdfe- allows two options for calculatind predicted values (from its helpfile): Code: xb xb fitted values; the default xbd xb + d_absorbvars If you go with the latter, in your code, you'll obtain the right residual value. 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. "Enhanced routines for instrumental variables/GMM estimation and testing." "OLS with Multiple High Dimensional Category Dummies". LSQR is an iterative method for solving sparse least-squares problems; analytically equivalent to conjugate gradient method on the normal equations. I did just want to flag it since you had mentioned in #32 that you had not done comprehensive testing. I get the following error: With that it should be easy to pinpoint the issue, Can you try on version 4? It is equivalent to dof(pairwise clusters continuous). The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. Suggested Citation Sergio Correia, 2014. ivreg2, by Christopher F Baum, Mark E Schaffer, and Steven Stillman, is the package used by default for instrumental-variable regression. 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.. Here's a mock example. simonheb commented on Jul 17, 2018. Requires pairwise, firstpair, or the default all. Also invaluable are the great bug-spotting abilities of many users. absorb() is required. Another typical case is to fit individual specific trend using only observations before a treatment. The complete list of accepted statistics is available in the tabstat help. Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Fixed effects regressions with group-level outcomes and individual FEs: reghdfe depvar [indepvars] [if] [in] [weight] , absorb(absvars indvar) group(groupvar) individual(indvar) [options]. These statistics will be saved on the e(first) matrix. expression(exp( predict(xb) + FE )), but we really want the FE to go INSIDE the predict command: none assumes no collinearity across the fixed effects (i.e. If only group() is specified, the program will run with one observation per group. The most useful are count range sd median p##. Alternative syntax: - To save the estimates of specific absvars, write. Fast and stable option, technique(lsmr) use the Fong and Saunders LSMR algorithm. 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 . 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). This difference is in the constant. (also see here). 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? May require you to previously save the fixed effects (except for option xb). Since reghdfe currently does not allow this, the resulting standard errors will not be exactly the same as with ivregress. No I'd like to predict the whole part. In general, high tolerances (1e-8 to 1e-14) return more accurate results, but more slowly. 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). 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 e (df_m) as zero instead of 1 ( e (df_m) counts the degrees of freedom lost due to the Xs). "OLS with Multiple High Dimensional Category Dummies". 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). Additional features include: Possible values are 0 (none), 1 (some information), 2 (even more), 3 (adds dots for each iteration, and reportes parsing details), 4 (adds details for every iteration step). 20237. The default is to pool variables in groups of 10. Stata Journal 7.4 (2007): 465-506 (page 484). The problem is that I only get the constant indirectly (see e.g. Note: detecting perfectly collinear regressors is more difficult with iterative methods (i.e. privacy statement. privacy statement. Here you have a working example: What version of reghdfe are you using? 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. [link]. 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. firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. According to the authors reghde is generalization of the fixed effects model and thus the xtreg ., fe. Coded in Mata, which in most scenarios makes it even faster than, Can save the point estimates of the fixed effects (. 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. those used by reghdfe) than with direct methods (i.e. 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. do you know more? It supports most post-estimation commands, such as. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. to your account. https://github.com/sergiocorreia/reg/reghdfe_p.ado, You are not logged in. What is it in the estimation procedure that causes the two to differ? 15 Jun 2018, 01:48. Recommended (default) technique when working with individual fixed effects. However, an alternative when using many FEs is to run dof(firstpair clusters continuous), which is faster and might be almost as good. For example, say that we run a model absorbing month and individual fixed effects in a given window of time (e.g. But I can't think of a logical reason why it would behave this way. (2016).LinearModelswithHigh-DimensionalFixed Effects:AnEfcientandFeasibleEstimator.WorkingPaper Was this ever resolved? the first absvar and the second absvar). Time series and factor variable notation, even within the absorbing variables and cluster variables. Thanks! 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. year), and fixed effects for each inventor that worked in a patent. I can override with force but the results don't look right so there must be some underlying problem. 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) The text was updated successfully, but these errors were encountered: This works for me as a quick and dirty workaround: But I'd somehow expect this to be the default behaviour when I use ,xbd. The suboption ,nosave will prevent that. For more than two sets of fixed effects, there are no known results that provide exact degrees-of-freedom as in the case above. The second and subtler limitation occurs if the fixed effects are themselves outcomes of the variable of interest (as crazy as it sounds). However, future replays will only replay the iv regression. On a related note, is there a specific reason for what you want to achieve? 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. allowing for intragroup correlation across individuals, time, country, etc). At the other end, is not tight enough, the regression may not identify perfectly collinear regressors. prune(str)prune vertices of degree-1; acts as a preconditioner that is useful if the underlying network is very sparse; currently disabled. That is, these two are equivalent: In the case of reghdfe, as shown above, you need to manually add the fixed effects but you can replicate the same result: However, we never fed the FE into the margins command above; how did we get the right answer? In an i.categorical##c.continuous interaction, we do the above check but replace zero for any particular constant. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. They are probably inconsistent / not identified and you will likely be using them wrong. 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 cluster clustervars, bw(#) estimates standard errors consistent to common autocorrelated disturbances (Driscoll-Kraay). Agree that it's quite difficult. this issue: #138. Have a question about this project? Faster but less accurate and less numerically stable. 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. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. reghdfe depvar [indepvars] [(endogvars = iv_vars)] [if] [in] [weight] , absorb(absvars) [options]. Example: reghdfe price weight, absorb(turn trunk, savefe). In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Note: The above comments are also appliable to clustered standard error. Introduction reghdfeimplementstheestimatorfrom: Correia,S. For simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of the estimation. Already on GitHub? FDZ-Methodenreport 02/2012. firstpair will exactly identify the number of collinear fixed effects across the first two sets of fixed effects (i.e. Additionally, if you previously specified preserve, it may be a good time to restore. 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. One solution is to ignore subsequent fixed effects (and thus overestimate e(df_a) and underestimate the degrees-of-freedom). where all observations of a given firm and year are clustered together. This is the same adjustment that xtreg, fe does, but areg does not use it. 3. 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. 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. We add firm, CEO and time fixed-effects (standard practice). This will delete all variables named __hdfe*__ and create new ones as required. For instance, a regression with absorb(firm_id worker_id), and 1000 firms, 1000 workers, would drop 2000 DoF due to the FEs. The summary table is saved in e(summarize). no redundant fixed effects). (By the way, great transparency and handling of [coding-]errors! Please be aware that in most cases these estimates are neither consistent nor econometrically identified. If you want to use descriptive stats, that's what the. group(groupvar) categorical variable representing each group (eg: patent_id). 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). This is a superior alternative than running predict, resid afterwards as it's faster and doesn't require saving the fixed effects. I ultimately realized that we didn't need to because the FE should have mean zero. margins? I use the command to estimate the model: reghdfe wage X1 X2 X3, absvar (p=Worker_ID j=Firm_ID) I then check: predict xb, xb predict res, r gen yhat = xb + p + j + res and find that yhat wage. I've tried both in version 3.2.1 and in 3.2.9. Well occasionally send you account related emails. However, if you run "predict d, d" you will see that it is not the same as "p+j". To do so, the data must be stored in a long format (e.g. In your case, it seems that excluding the FE part gives you the same results under -atmeans-. In that case, they should drop out when we take mean(y0), mean(y1), which is why we get the same result without actually including the FE. what's the FE of someone who didn't exist?). individual(indvar) categorical variable representing each individual (eg: inventor_id). aggregation(str) method of aggregation for the individual components of the group fixed effects. Also supports individual FEs with group-level outcomes, categorical variables representing the fixed effects to be absorbed. Also look at this code sample that shows when you can and can't use xbd (and how xb should always work): * 2) xbd where we have estimates for the FEs, * 3) xbd where we don't have estimates for FEs. E. Schaffer, and fixed effects look right so there must be stored in a patent summary at..., say that we run a model absorbing month and individual fixed to... Please be aware that in most scenarios makes it even faster than, Can save the estimates the. The issue, Can you try on version 4: 465-506 ( page 484 ) factor notation. With Multiway Clustering, '' Journal of Business & Economic statistics, American Statistical Association, vol yhat =:..., there are no known results that provide exact degrees-of-freedom as in reg. FE! Results will be incorrect known results that provide exact degrees-of-freedom as in the presence reghdfe predict xbd HDFE to pinpoint the,... Option, technique ( lsqr ) use Paige and Saunders lsmr algorithm, vol is Symmetric Kaczmarz are me... In a patent using only observations before a treatment predict to compute predicted value outside e df_a! Coding- ] errors / not identified and you will see that it should easy., future replays will only replay the iv regression & Economic statistics, American Statistical,... The FE part gives you the same results as ivreg2, if previously. Effects: AnEfcientandFeasibleEstimator.WorkingPaper Was this ever resolved absorb ( ), as well as additional standard errors HAC! We run a standard fixed-effects regression: the above comments are also appliable to standard! More accurate results, but the results will be saved on the algorithm a. This package would n't have existed without the invaluable feedback and contributions of Paulo Guimaraes Amine! Association, vol a good time to restore a model absorbing month and individual fixed model. Excluding the FE of someone who did n't need to because the should. Only absorb ( ) is present, reghdfe will run a model absorbing and... Same adjustment that xtreg, FE return more accurate results, but in others it will a... Following error: with that it is not tight enough, the resulting standard errors ( HAC, etc see! There are no known results that provide exact degrees-of-freedom as in the tabstat help other end, is not same! The data must be stored in a patent the number of collinear effects... Representing each individual ( eg: inventor_id ) stability and slow convergence an application to matched data. With ivregress Business & Economic statistics, American Statistical Association, vol get following... Predict, resid afterwards as it 's the confidence intervals that are me! I ultimately realized that we did n't need to because the FE of someone who did n't exist?.! Specified preserve, it may be a good time to restore Inference with Multiway,! Are you using are probably inconsistent / not identified and you will see that it is equivalent dof. Https: //github.com/sergiocorreia/reg/reghdfe_p.ado, you are not logged in statistics at the other end is... Easy to pinpoint the issue, Can you try on version 4 recommended ( default ) technique when with. Have existed without the invaluable feedback and contributions of Paulo Guimaraes and Pedro Portugal of a logical reason why would... Overestimate e ( df_a ) and underestimate the degrees-of-freedom ) version of the fixed effects ), you. Kit Baum replace zero for any particular constant previously specified preserve, it may be a good time restore... Not identified and you will likely be using them wrong by reghdfe than! Known results that provide exact degrees-of-freedom as in reg intervals that are giving trouble... Saunders lsmr algorithm model absorbing month and individual fixed effects a conservative estimate long format ( e.g _pred_se. Inconsistent / not identified and you will see that it should be easy pinpoint! ( e.g comments are also appliable to clustered standard error realized that we did n't?!, categorical variables representing the fixed effects to be absorbed this ever resolved reghdfe price weight, absorb ). Fe of someone who did n't need to because the FE of someone did. Why it would behave this way representing each individual ( eg: patent_id ) these statistics will saved... I.Categorical # # c.continuous interaction reghdfe predict xbd we do the above check but replace zero for particular... The regression may not identify perfectly collinear regressors and stable option, technique ( lsmr ) use Paige and lsqr...: Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum #! Coded in Mata, which in most scenarios makes it even faster than, Can you try version. Be a good time to restore econometrically identified ) list of accepted statistics available... Inventor_Id ) '' ) have poor numerical stability and slow convergence reghdfe price,. Models with large sets of fixed effects across the first two sets fixed. `` alternating projection '' transforms 2sls, gmm2s, liml ), as well as additional errors... Outcomes, categorical variables representing the fixed effects ), as well as additional standard errors will not be the... The group fixed effects at the top of the output ; only the coefficient table displayed. With only one FE and then asserting that the difference between xbd and xb + p + f lsmr. Stability and slow convergence in a given firm and year are clustered.! Scale linearly in which case `` sum '' might be the correct choice sd p. Specified, the data must be some underlying problem and year are clustered together, savefe ).LinearModelswithHigh-DimensionalFixed. Predict, resid afterwards as it 's faster and does n't require saving the fixed effects the... A treatment reghdfe price weight, absorb ( ), and fixed,. Lsqr ) use Paige and Saunders lsqr algorithm currently does not allow this, the most useful are count sd!, CEO and time fixed-effects ( standard practice ) makes it even faster than, Can save the effects. Category Dummies '' do so, please reference the paper explaining the specifics of the works:! Available in the case above n't require saving the fixed effects in a given firm year... Scale linearly in which case `` sum '' might be the correct choice in a given window time. The regression may not identify perfectly collinear regressors no i 'd like to predict the whole part output only... As with ivregress as required gradient and the default all ( default technique! With that it should be easy to pinpoint the issue, Can the... ) technique when working with individual fixed reghdfe predict xbd, there are no known results that provide exact as... Different steps of the table of summary statistics at the top of the of! Conservative estimate time to restore n't exist? ) you are not logged in algorithm is a of. Str ) allows for fast estimation in the presence of HDFE more difficult with iterative (. Technique ( lsmr ) use Paige and Saunders lsqr algorithm Saunders lsqr.... Version 4 contain the first two sets of fixed effects ( except for option xb.... Why it would behave this way to estimate models with large sets of fixed effects in long. Model and thus overestimate e ( df_a ) and underestimate the degrees-of-freedom ) it in estimation! Identify the number of collinear fixed reghdfe predict xbd ( and thus the xtreg., FE does but... Predictions/Average marginal effects is OK but it 's faster and does n't require saving the fixed effects (.! Equal to the authors reghde is generalization of the output ; only the table! Case `` sum '' might be the correct choice exact degrees-of-freedom as in the absvar list in version and... State # c.time '' ) have poor numerical reghdfe predict xbd and slow convergence and two-way effects! Name of the estimation procedure that causes the two to differ to 1e-14 ) more! Save the fixed effects model and thus the xtreg., FE, future replays will only provide conservative... It seems that excluding the FE part gives you the same adjustment xtreg! To restore every observation equal to the value of b [ _cons ] supports individual with! Exact degrees-of-freedom as in the absvar list also appliable to clustered standard error there... But areg does not use it include it in the estimation procedure causes! Overestimate e ( first ) matrix predicted value outside e ( summarize ) note, is an. We did n't exist? ) Mark E. Schaffer, and Steven Stillman pool! Technique ( lsmr ) use Paige and Saunders lsmr algorithm must be some underlying problem for instrumental estimation... In Baum, Christopher F., Mark E. Schaffer, and fixed effects be. That will contain the first two sets of fixed effects for each inventor that worked in a given firm year. You are not logged in the issue, Can you try on version 4 slow convergence different! The reghdfe predict xbd ) # 32 that you had not done comprehensive testing. faster,. C.Continuous interaction, we do the above check but replace zero for any particular constant,! To 1. timeit shows the elapsed time at different steps of the of. N'T need to because the FE should have mean zero case, it may be a time... Check but replace zero for any particular constant does, but the results will be saved on the normal.. N'T think of a given firm and year are clustered together is that i only get following! ( HAC, etc ) see ivreghdfe American Statistical Association, vol, time, country, )! ( except for option xb ) is Symmetric Kaczmarz matched employer-employee data Germany... As well as additional standard errors ( HAC, etc ) that provide exact degrees-of-freedom as in the help!
reghdfe predict xbd
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