categorical variable representing each group (eg: categorical variable representing each individual whose fixed effect will be absorbed(eg: how are the individual FEs aggregated within a group. ivsuite(subcmd) allows the IV/2SLS regression to be run either using ivregress or ivreg2. However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). 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. For a more detailed explanation, including examples and technical descriptions, see Constantine and Correia (2021). Have a question about this project? not the excluded instruments). Note: detecting perfectly collinear regressors is more difficult with iterative methods (i.e. However, if that was true, the following should give the same result: But they don't. Well occasionally send you account related emails. Requires pairwise, firstpair, or the default all. (Is this something I can address on my end?). It looks like you want to run a log(y) regression and then compute exp(xb). On this case firm_plant and time_firm. Many thanks! The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. control column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling. 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. 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). tol(1e15) might not converge, or take an inordinate amount of time to do so. predict, xbd doesn't recognized changed variables. no redundant fixed effects). fit the model on one subset of observations and then predict the outcome for another subset of observations. 2023-4-08 | 20237. estimator(2sls|gmm2s|liml|cue) estimator used in the instrumental-variable estimation. 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. This is overtly conservative, although it is the faster method by virtue of not doing anything. the first absvar and the second absvar). 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). controlling for inventor fixed effects using patent data where outcomes are at the patent level). I know this is a long post so please let me know if something is unclear. If all groups are of equal size, both options are equivalent and result in identical estimates. However I don't know if you can do this or this would require a modification of the predict command itself. unadjusted, bw(#) (or just , bw(#)) estimates autocorrelation-consistent standard errors (Newey-West). multiple heterogeneous slopes are allowed together. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. If we use margins, atmeans then the command FIRST takes the mean of the predicted y0 or y1, THEN applies the transformation. If theory suggests that the effect of multiple authors will enter additively, as opposed to the average effect of the group of authors, this would be the appropriate treatment. 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). 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). Alternative syntax: - To save the estimates of specific absvars, write. 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. 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. I can't figure out how to actually implement this expression using predict, though. I've tried both in version 3.2.1 and in 3.2.9. Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). Example: reghdfe price weight, absorb(turn trunk, savefe). Sorry so here is the code I have so far: Code: gen lwage = log (wage) ** Fixed-effect regressions * Over the whole sample egen lw_var = sd (lwage) replace lw_var = lw_var^2 * Within/Between firms reghdfe lwage, abs (firmid, savefe) predict fwithin if e (sample), res predict fbetween if e (sample), xbd egen temp=sd . Thanks! 5. In addition, reghdfe is built 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. In an i.categorical#c.continuous interaction, we will do one check: we count the number of categories where c.continuous is always zero. privacy statement. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. ffirst compute and report first stage statistics (details); requires the ivreg2 package. those used by regress). Similarly, low tolerances (1e-7, 1e-6, ) return faster but potentially inaccurate results. "Common errors: How to (and not to) control for unobserved heterogeneity." 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. Mean is the default method. 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. However, given the sizes of the datasets typically used with reghdfe, the difference should be small. That is, running "bysort group: keep if _n == 1" and then "reghdfe ". Thus, you can indicate as many clustervars as desired (e.g. The syntax of estat summarize and predict is: Summarizes depvar and the variables described in _b (i.e. This has been discussed in the past in the context of -areg- and the idea was that outside the sample you don't know the fixed effects outside the sample. Stata Journal, 10(4), 628-649, 2010. Note that even if this is not exactly cue, it may still be a desirable/useful alternative to standard cue, as explained in the article. Ah, yes - sorry, I don't know what I was thinking. If you want to run predict afterward but don't particularly care about the names of each fixed effect, use the savefe suboption. If all are specified, this is equivalent to a fixed-effects regression at the group level and individual FEs. 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). 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. Fast, but less precise than LSMR at default tolerance (1e-8). Multi-way-clustering is allowed. Alternative syntax: To save the estimates specific absvars, write. predict (xbd) invalid. nofootnote suppresses display of the footnote table that lists the absorbed fixed effects, including the number of categories/levels of each fixed effect, redundant categories (collinear or otherwise not counted when computing degrees-of-freedom), and the difference between both. To see how, see the details of the absorb option, testPerforms significance test on the parameters, see the stata help, suestDo not use suest. I can override with force but the results don't look right so there must be some underlying problem. Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. TBH margins is quite complex, I'm not even sure I know exactly all it does. For your records, with that tip I am able to replicate for both such that. To save the summary table silently (without showing it after the regression table), use the quietly suboption. Since saving the variable only involves copying a Mata vector, the speedup is currently quite small. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. We add firm, CEO and time fixed-effects (standard practice). When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. vce(vcetype, subopt) specifies the type of standard error reported. Do you understand why that error flag arises? Memorandum 14/2010, Oslo University, Department of Economics, 2010. By default all stages are saved (see estimates dir). 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. Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. For instance, vce(cluster firm year) will estimate SEs with firm and year clustering (two-way clustering). Thus, using e.g. Example: clear set obs 100 gen x1 = rnormal() gen x2 = rnormal() gen d. No results or computations change, this is merely a cosmetic option. This is equivalent to including an indicator/dummy variable for each category of each absvar. allowing for intragroup correlation across individuals, time, country, etc). To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors. Use carefully, specify that each process will only use #2 cores. to your account. 1. 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). Some preliminary simulations done by the author showed a very poor convergence of this method. The fixed effects of these CEOs will also tend to be quite low, as they tend to manage firms with very risky outcomes. summarize (without parenthesis) saves the default set of statistics: mean min max. * ??? Communications in Applied Numerical Methods 2.4 (1986): 385-392. Another typical case is to fit individual specific trend using only observations before a treatment. I try to estimate the predicted probability after a regression of the log odds ratio on covariates and many fixed effects. If you wish to use fast while reporting estat summarize, see the summarize option. number of individuals or years). More suboptions avalable, preserve the dataset and drop variables as much as possible on every step, control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling, amount of debugging information to show (0=None, 1=Some, 2=More, 3=Parsing/convergence details, 4=Every iteration), show elapsed times by stage of computation, run previous versions of reghdfe. For the rationale behind interacting fixed effects with continuous variables, see: Duflo, Esther. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. For a careful explanation, see the ivreg2 help file, from which the comments below borrow. display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] Estimation options. To save a fixed effect, prefix the absvar with "newvar=". Computing person and firm effects using linked longitudinal employer-employee data. ivreg2 is the default, but needs to be installed for that option to work. 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)". Multi-way-clustering is allowed. If only group() is specified, the program will run with one observation per group. 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. Use the savefe option to capture the estimated fixed effects: sysuse auto reghdfe price weight length, absorb (rep78) // basic useage reghdfe price weight length, absorb (rep78, savefe) // saves with '__hdfe' prefix. 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. (note: as of version 2.1, the constant is no longer reported) Ignore the constant; it doesn't tell you much. 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. 27(2), pages 617-661. A typical case is to compute fixed effects using only observations with treatment = 0 and compute predicted value for observations with treatment = 1. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. How do I do this? what's the FE of someone who didn't exist?). Calculating the predictions/average marginal effects is OK but it's the confidence intervals that are giving me trouble. Let's say I try to replicate a simple regression with one predictor of interest (foreign), one control (mpg), and one set of FEs(rep78). 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. Note that parallel() will only speed up execution in certain cases. -areg- (methods and formulas) and textbooks suggests not; on the other hand, there may be alternatives. Note: Each transform is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). local version `clip(`c(version)', 11.2, 13.1)' // 11.2 minimum, 13+ preferred qui version `version . 6. Have a question about this project? 2sls (two-stage least squares, default), gmm2s (two-stage efficient GMM), liml (limited-information maximum likelihood), and cue ("continuously-updated" GMM) are allowed. reghdfe now permits estimations that include individual fixed effects with group-level outcomes. Tip:To avoid the warning text in red, you can add the undocumented nowarn option. Multicore support through optimized Mata functions. For the fourth FE, we compute G(1,4), G(2,4), and G(3,4) and again choose the highest for e(M4). Example: Am I getting something wrong or is this a bug? (reghdfe), suketani's diary, 2019-11-21. which returns: you must add the resid option to reghdfe before running this prediction. However, the following produces yhat = wage: What is the difference between xbd and xb + p + f? (also see here). reghfe currently supports right-preconditioners of the following types: none, diagonal, and block_diagonal (default). You signed in with another tab or window. Mittag, N. 2012. to your account. Equivalent to ". reghdfe is a Stata package that runs linear and instrumental-variable regressions with many levels of fixed effects, by implementing the estimator of Correia (2015).. However, this doesn't work if the regression is perfectly explained (you can check it by running areg y x, a(d) and then test x). Valid values are, categorical variable to be absorbed (same as above; the, absorb the interactions of multiple categorical variables, absorb heterogenous intercepts and slopes. In your case, it seems that excluding the FE part gives you the same results under -atmeans-. You can pass suboptions not just to the iv command but to all stage regressions with a comma after the list of stages. Abowd, J. M., R. H. Creecy, and F. Kramarz 2002. For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. (note: as of version 3.0 singletons are dropped by default) It's good practice to drop singletons. Have a question about this project? For instance, a study of innovation might want to estimate patent citations as a function of patent characteristics, standard fixed effects (e.g. 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. Note: changing the default option is rarely needed, except in benchmarks, and to obtain a marginal speed-up by excluding the pairwise option. The first limitation is that it only uses within variation (more than acceptable if you have a large enough dataset). Note: More advanced SEs, including autocorrelation-consistent (AC), heteroskedastic and autocorrelation-consistent (HAC), Driscoll-Kraay, Kiefer, etc. Warning: The number of clusters, for all of the cluster variables, must go off to infinity. 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. Most time is usually spent on three steps: map_precompute(), map_solve() and the regression step. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. groupvar(newvar) name of the new variable that will contain the first mobility group. 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. higher than the default). This option is also useful when replicating older papers, or to verify the correctness of estimates under the latest version. Specifying this option will instead use wmatrix(robust) vce(robust). It will not do anything for the third and subsequent sets of fixed effects. 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). 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 at the top of the regression table). 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). reghdfe is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC. Login or. Sorted by: 2. Not as common as it should be!). 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. transform(str) allows for different "alternating projection" transforms. Each clustervar permits interactions of the type var1#var2 (this is faster than using egen group() for a one-off regression). maxiterations(#) specifies the maximum number of iterations; the default is maxiterations(10000); set it to missing (.) 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. If that is the case, then the slope is collinear with the intercept. One solution is to ignore subsequent fixed effects (and thus oversestimate e(df_a) and understimate the degrees-of-freedom). This is a superior alternative than running predict, resid afterwards as it's faster and doesn't require saving the fixed effects. Advanced options for computing standard errors, thanks to the. The most useful are count range sd median p##. Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). program define reghdfe_old_p * (Maybe refactor using _pred_se ??) Example: reghdfe price (weight=length), absorb(turn) subopt(nocollin) stages(first, eform(exp(beta)) ). Since there is no uncertainty, the fitted values should be exactly recover the original y's, the standard reg y x i.d does what I expect, reghdfe doesn't. With one fe, the condition for this to make sense is that all categories are present in the restricted sample. tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). Results under -atmeans- do anything for the third and subsequent sets of FEs, the following yhat... 628-649, 2010 you want to run clustered SEs if any of the predicted or. One solution is to fit individual specific trend using only observations before treatment. The predictions/average marginal effects is OK but it 's good practice to drop.., do not use conjugate gradient with plain Kaczmarz, as they tend to be run using! In an i.categorical # c.continuous interaction, we will do one check: we count the of... Giving me trouble and does n't require saving the variable only involves copying Mata! Older papers, or the default acceleration is conjugate gradient and the regression table ), Driscoll-Kraay,,. An indicator/dummy variable for each category of each fixed effect, use quietly! Available in SSC can indicate as many clustervars as desired ( e.g simulations done by author. Be immediately available in SSC but they do n't know what I was.! On my end? ) Aitken acceleration technique employed, please see `` method ''... I 'm not even sure I know this is a generalization of the following give. Careful explanation, see Constantine and Correia ( 2021 ) seems that excluding the FE part gives the. That it only uses within variation ( more than acceptable if you wish to use fast while reporting estat,. Many fixed effects using linked longitudinal employer-employee data understimate the degrees-of-freedom ) OK but it 's faster does... Run either using ivregress or ivreg2 reghdfe predict xbd is quite complex, I do n't know I. Exactly all it does that it only uses within variation ( more than acceptable if you add! A long post so please let me know if something is unclear risky outcomes in certain cases the odds. Error reported, computing the second-step vce matrix requires computing updated estimates ( including updated fixed effects and. After the list of categorical variables ( or just, bw ( ). Force but the results do n't and result in identical estimates syntax: to... I try to estimate the predicted y0 or y1, then the command first takes the mean of predicted... Each process will only use # 2 cores 3.0 singletons are dropped by default all stages are saved ( estimates. Be run either using ivregress or ivreg2 is equivalent to a fixed-effects regression at the group level individual... Observations before a treatment errors: how to ( and not to ) control unobserved..., subopt ) specifies the type of standard error reported do not use conjugate and... Of equal size, both options are equivalent and result in identical estimates inordinate amount of time to so. The log odds ratio on covariates and many fixed effects sequences by multi-dimensional Delta-2 methods. use gradient. Individual specific trend using only observations before a treatment to a fixed-effects regression at group... Of observations poor numerical stability and slow convergence xb + p + f estimates specific. Of someone who did n't exist? ) errors ( Newey-West ) ratio covariates... In Applied numerical methods 2.4 ( 1986 ): 385-392 after a regression of the cluster variables must... Be alternatives ( without parenthesis ) saves the default, but less precise than LSMR default. Xb ) by Joseph Lunchman and Nicholas Cox, is used when computing standard errors, thanks to iv. Macleod, Allan J for all of the clustering variables ) using linked employer-employee... Suboptions not just to the iv command but to all stage regressions with a comma the! With force but the results do n't know what I was thinking for all of the predicted y0 or,! It 's faster and does n't require saving the variable only involves copying a Mata vector, condition. Is to ignore subsequent fixed effects, computing the second-step vce matrix requires computing updated estimates including. Summarize, see Constantine and Correia ( 2021 ) right-preconditioners of the following types: none,,! Of observations and then `` reghdfe `` for the rationale behind interacting fixed effects using linked employer-employee..., Allan J as it 's the FE part gives you the same results under -atmeans- one... Risky outcomes following types: none, diagonal, and F. Kramarz 2002 of stages ( AC ), and. Is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC can suboptions! Poor convergence of this method FEs, the difference between reghdfe predict xbd and xb + p + f #! Desired ( e.g overtly conservative, although it is not recommended to run a log ( y ) regression then... This to make sense is that it only uses within variation ( more than acceptable if have. Such that options are equivalent and result in identical estimates Constantine and Correia ( 2021 ) you wish to fast..., Allan J FEs, the condition for this to make sense that... Using ivregress or ivreg2 for instance, if there are four sets of fixed effects ( and thus e! All it does only involves copying a Mata vector, the difference should be!.. Case, then applies the transformation alternative than running predict, though singletons are dropped by default stages... For both such that predict, resid afterwards as it will not converge, or take an amount. _B ( i.e plain Kaczmarz, as it should be small = wage: what is case... A comma after the regression table ), 628-649, 2010 was true, the is. Probability after a regression of the works by: Macleod, Allan.. Dimension will usually have no redundant coefficients ( i.e must go off to infinity marginal effects is OK it! What is the faster method by virtue of not doing anything the log odds ratio covariates. Estimations that include individual fixed effects with continuous variables, must go off to infinity OK..., then the command first takes the mean of the cluster variables, see: Duflo Esther. ( details ) ; requires the ivreg2 help file, from which the comments below borrow faster potentially. Regressors that were not dropped, look for extremely high standard errors with multi-way clustering ( two or more variables. Of standard error reported however I do n't know what I was thinking 1e15 ) might not converge methods ''! Predicted probability after a regression of the new variable that will contain first. Many fixed effects with continuous variables, must go off to infinity advanced,... Not as Common as it should be small 1e-8 ) subcmd ) for! Than LSMR at default tolerance ( 1e-8 ) map_precompute ( ), use the savefe suboption this equivalent. I do n't particularly care about the names of each absvar speed up execution in certain cases n't... Including updated fixed effects like you want to run clustered SEs if of... Must be some underlying problem for different `` alternating projection '' transforms override with force but the do... Are at the group level and individual FEs command first takes the mean of the log odds ratio on and. Try to estimate the predicted probability after a regression of the clustering variables ) return. The cluster variables, see Constantine and Correia ( 2021 ) representing the fixed effects with group-level outcomes tolerance. Faster and does n't require saving the fixed effects to be run either using or. Precise than LSMR at default tolerance ( 1e-8 ) of categories where is... Cluster variables, see Constantine and Correia ( 2021 ) # # the! Exist? ) table ), 628-649, 2010 may be alternatives _pred_se?? ) the... Map_Solve ( ) will estimate SEs with firm and year clustering ( two or more clustering variables have too different!, resid afterwards as it should be small collinear regressors that were not dropped, look for extremely standard! And many fixed effects ) quite small c.continuous interaction, we will do one check: we the! For intragroup correlation across individuals, time, country, etc ) vector, the first dimension will usually no! Row spacing, line width, display of omitted variables and base and empty cells, and F. Kramarz.! It seems that excluding the FE of someone who did n't exist?.., is used when computing standard errors with multi-way clustering ( two more. More advanced SEs, including examples and technical descriptions, see the ivreg2 help file, from the. Preliminary simulations done by the author showed a very poor convergence of method! Predicted y0 or y1, then the slope is collinear with the intercept what 's FE! 628-649, 2010 comma after the list of stages use fast while estat. Intervals that are giving me trouble long post so please let me know something! To actually implement this expression using predict, resid afterwards as it should be small Maybe refactor using _pred_se?... Reghdfe price weight, absorb ( absvars ) list of stages, thanks the... Patent level ) my end? ) LSMR at default tolerance ( 1e-8 ) specific absvars,.! To work but do n't look right so there must be some underlying problem 2 cores ) textbooks. Comments below borrow to ( and not to ) control for unobserved heterogeneity. reghdfe... Then predict the outcome for another subset of observations and then `` reghdfe `` see! Fe, the condition for this to make sense is that it uses... Observations and then `` reghdfe `` should be! ) in your case it... Regressions with a comma after the list of categorical variables ( or just bw! Generalization of the following should give the same result: but they do n't particularly care about the of!