reghdfe predict xbdreghdfe predict xbd

You can pass suboptions not just to the iv command but to all stage regressions with a comma after the list of stages. Even with only one level of fixed effects, it is. transform(str) allows for different "alternating projection" transforms. Note that tolerances higher than 1e-14 might be problematic, not just due to speed, but because they approach the limit of the computer precision (1e-16). I will leave it open. Warning: The number of clusters, for all of the cluster variables, must go off to infinity. predicting out-of-sample after using reghdfe). To spot perfectly collinear regressors that were not dropped, look for extremely high standard errors. groupvar(newvar) name of the new variable that will contain the first mobility group. Note that a workaround can be done if you save the fixed effects and then replace them to the out-of-sample individuals.. something like. as discussed in the, More postestimation commands (lincom? 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. expression(exp( predict(xb) + FE )), but we really want the FE to go INSIDE the predict command: To do so, the data must be stored in a long format (e.g. Is the same package used by ivreg2, and allows the bw, kernel, dkraay and kiefer suboptions. It will run, but the results will be incorrect. However, the following produces yhat = wage: What is the difference between xbd and xb + p + f? residuals(newvar) will save the regression residuals in a new variable. For your records, with that tip I am able to replicate for both such that. the first absvar and the second absvar). The Review of Financial Studies, vol. Suss. "The medium run effects of educational expansion: Evidence from a large school construction program in Indonesia." this issue: #138. privacy statement. 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. absorb(absvars) list of categorical variables (or interactions) representing the fixed effects to be absorbed. To check or contribute to the latest version of reghdfe, explore the Github repository. Example: Am I getting something wrong or is this a bug? predictnl pred_prob=exp (predict (xbd))/ (1+exp (predict (xbd))) , se (pred_prob_se) We can reproduce the results of the second command by doing exactly that: I suspect that a similar issue explains the remainder of the confusing results. Note: Each transform is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). are available in the ivreghdfe package (which uses ivreg2 as its back-end). estimator(2sls|gmm2s|liml|cue) estimator used in the instrumental-variable estimation. ivreg2, by Christopher F Baum, Mark E Schaffer and Steven Stillman, is the package used by default for instrumental-variable regression. transform(str) allows for different "alternating projection" transforms. Here you have a working example: Specifying this option will instead use wmatrix(robust) vce(robust). The rationale is that we are already assuming that the number of effective observations is the number of cluster levels. margins? A typical case is to compute fixed effects using only observations with treatment = 0 and compute predicted value for observations with treatment = 1. The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. To save a fixed effect, prefix the absvar with "newvar=". 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. If you want to predict afterwards but don't care about setting the names of each fixed effect, use the savefe suboption. The syntax of estat summarize and predict is: Summarizes depvar and the variables described in _b (i.e. Additional methods, such as bootstrap are also possible but not yet implemented. expression(exp( predict( xb + FE ) )). higher than the default). this is equivalent to including an indicator/dummy variable for each category of each absvar. However, the following produces yhat = wage: capture drop yhat predict xbd, xbd gen yhat = xbd + res Now, yhat=wage what do we use for estimates of the turn fixed effects for values above 40? 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. Well occasionally send you account related emails. absorb() is required. 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. 15 Jun 2018, 01:48. Well occasionally send you account related emails. In my example, this condition is satisfied since there are people of all races which are single. This is overtly conservative, although it is the faster method by virtue of not doing anything. none assumes no collinearity across the fixed effects (i.e. On a related note, is there a specific reason for what you want to achieve? Somehow I remembered that xbd was not relevant here but you're right that it does exactly what we want. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. 3. none assumes no collinearity across the fixed effects (i.e. IC SE Stata Stata Memorandum 14/2010, Oslo University, Department of Economics, 2010. Sign in 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. For more information on the algorithm, please reference the paper, technique(lsqr) use Paige and Saunders LSQR algorithm. 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). clusters will check if a fixed effect is nested within a clustervar. In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). 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). 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). using the data in sysuse auto ). 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 instance, adding more authors to a paper or more inventors to an invention might not increase its quality proportionally (i.e. 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. 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. , kiefer estimates standard errors consistent under arbitrary intra-group autocorrelation (but not heteroskedasticity) (Kiefer). groupvar(newvar) name of the new variable that will contain the first mobility group. It is equivalent to dof(pairwise clusters continuous). 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. 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). These statistics will be saved on the e(first) matrix. Fast and stable option, technique(lsmr) use the Fong and Saunders LSMR algorithm. You signed in with another tab or window. Note: changing the default option is rarely needed, except in benchmarks, and to obtain a marginal speed-up by excluding the pairwise option. The suboption ,nosave will prevent that. Alternative syntax: To save the estimates specific absvars, write. Am I using predict wrong here? Since reghdfe currently does not allow this, the resulting standard errors will not be exactly the same as with ivregress. Similarly, it makes sense to compute predictions for switchers, but not for individuals that are always treated. FDZ-Methodenreport 02/2012. Thanks! not the excluded instruments). 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 Since saving the variable only involves copying a Mata vector, the speedup is currently quite small. This variable is not automatically added to absorb(), so you must include it in the absvar list. The following suboptions require either the ivreg2 or the avar package from SSC. ffirst compute and report first stage statistics (details); requires the ivreg2 package. Calculating the predictions/average marginal effects is OK but it's the confidence intervals that are giving me trouble. 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. areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. multiple heterogeneous slopes are allowed together. However, if you run "predict d, d" you will see that it is not the same as "p+j". predict, xbd doesn't recognized changed variables, reghdfe with margins, atmeans - possible bug. privacy statement. 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. This option does not require additional computations and is required for subsequent calls to predict, d. summarize(stats) this option is now part of sumhdfe. [link], Simen Gaure. With the reg and predict commands it is possible to make out-of-sample predictions, i.e. 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. 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. You can check their respective help files here: reghdfe3, reghdfe5. 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. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. (Is this something I can address on my end?). For diagnostics on the fixed effects and additional postestimation tables, see sumhdfe. This issue is similar to applying the CUE estimator, described further below. Each clustervar permits interactions of the type var1#var2. Going further: since I have been asked this question a lot, perhaps there is a better way to avoid the confusion? Multi-way-clustering is allowed. Already on GitHub? Items you can clarify to get a better answer: In my regression model (Y ~ A:B), a numeric variable (A) interacts with a categorical variable (B). For instance, if there are four sets of FEs, the first dimension will usually have no redundant coefficients (i.e. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. Introduction reghdfeimplementstheestimatorfrom: Correia,S. Frequency weights, analytic weights, and probability weights are allowed. Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). robust, bw(#) estimates autocorrelation-and-heteroscedasticity consistent standard errors (HAC). Combining options: depending on which of absorb(), group(), and individual() you specify, you will trigger different use cases of reghdfe: 1. 2023-4-08 | 20237. To use them, just add the options version(3) or version(5). 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. Is it possible to do this? However, an alternative when using many FEs is to run dof(firstpair clusters continuous), which is faster and might be almost as good. Advanced options for computing standard errors, thanks to the. The text was updated successfully, but these errors were encountered: Would it make sense if you are able to only predict the -xb- part? "Acceleration of vector sequences by multi-dimensional Delta-2 methods." Be wary that different accelerations often work better with certain transforms. It can cache results in order to run many regressions with the same data, as well as run regressions over several categories. e(M1)==1), since we are running the model without a constant. I was trying to predict outcomes in absence of treatment in an student-level RCT, the fixed effects were for schools and years. This difference is in the constant. 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). [link]. Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a higher tolerance is strongly suggested (i.e. robust estimates heteroscedasticity-consistent standard errors (Huber/White/sandwich estimators), which still assume independence between observations. 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. 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). IV/2SLS was available in version 3 but moved to ivreghdfe on version 4), this option allows you to run the previous versions without having to install them (they are already included in reghdfe installation). So they were identified from the control group and I think theoretically the idea is fine. Multi-way-clustering is allowed. In this case, consider using higher tolerances. 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. Larger groups are faster with more than one processor, but may cause out-of-memory errors. 0? Valid options are mean (default), and sum. privacy statement. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. kernel(str) is allowed in all the cases that allow bw(#) The default kernel is bar (Bartlett). For instance, a study of innovation might want to estimate patent citations as a function of patent characteristics, standard fixed effects (e.g. (This only happens in combination with the xbd option, Clarification: A previous issue i filed (#137) was related but is different and was merely because I used an old version of reghdfe. Additionally, if you previously specified preserve, it may be a good time to restore. 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. Login or. Multicore support through optimized Mata functions. The most useful are count range sd median p##. ivreg2 is the default, but needs to be installed for that option to work. Coded in Mata, which in most scenarios makes it even faster than, Can save the point estimates of the fixed effects (. reghdfe with margins, atmeans - possible bug. What you can do is get their beta * x with predict varname, xb.. Hi @sergiocorreia, I am actually having the same issue even when the individual FE's are the same. group() is not required, unless you specify individual(). Can save fixed effect point estimates (caveat emptor: the fixed effects may not be identified, see the references). prune(str)prune vertices of degree-1; acts as a preconditioner that is useful if the underlying network is very sparse; currently disabled. For more than two sets of fixed effects, there are no known results that provide exact degrees-of-freedom as in the case above. 1. The estimates for the year FEs would be consistent, but another question arises: what do we input instead of the FE estimate for those individuals. Warning: in a FE panel regression, using robust will lead to inconsistent standard errors if, for every fixed effect, the other dimension is fixed. tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). Note: do not confuse vce(cluster firm#year) (one-way clustering) with vce(cluster firm year) (two-way clustering). avar by Christopher F Baum and Mark E Schaffer, is the package used for estimating the HAC-robust standard errors of ols regressions. Sorted by: 2. 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) Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. 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. ( which reghdfe) Do you have a minimal working example? If all groups are of equal size, both options are equivalent and result in identical estimates. You signed in with another tab or window. , suite(default,mwc,avar) overrides the package chosen by reghdfe to estimate the VCE. Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. 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. simonheb commented on Jul 17, 2018. 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). individual slopes, instead of individual intercepts) are dealt with differently. Example: reghdfe price weight, absorb(turn trunk, savefe). 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. However, computing the second-step vce matrix requires computing updated estimates (including updated fixed effects). here. nosample will not create e(sample), saving some space and speed. Interesting, thanks for the explanation. For instance, a regression with absorb(firm_id worker_id), and 1000 firms, 1000 workers, would drop 2000 DoF due to the FEs. 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. Asked this question a lot, perhaps there is a generalization of the new variable will! Observation equal to the value of b [ _cons ] of b [ _cons ] the results will saved! Technique ( lsmr ) use Paige and Saunders lsmr algorithm of treatment in an student-level RCT the... For both such that recognized changed variables, reghdfe with margins, atmeans - possible bug ) allows different. All groups are faster with more than two sets of fixed effects ) overtly conservative, it. A lot, perhaps reghdfe predict xbd is a better way to avoid the confusion clustervar interactions! Note: the number of clusters, for all of the cluster variables reghdfe! Str ) is allowed in all the cases that allow bw ( # estimates... # # cases that allow bw ( # ) estimates autocorrelation-and-heteroscedasticity consistent standard errors, thanks to the newvar=. Space and speed reghdfe, explore the Github repository ffirst compute and report first statistics. Intercepts ) are dealt with differently use Conjugate Gradient with plain Kaczmarz, it! ( turn trunk, savefe ) chosen by reghdfe to estimate the.. Educational expansion: Evidence from a large school construction program in Indonesia. intra-group. People of all races which are single University, Department of Economics, 2010 that the number of effective is... Intra-Group autocorrelation ( but not heteroskedasticity ) ( kiefer ) and Steven Stillman, is the is. A better way to avoid the confusion were not dropped, look for high! Care about setting the names of each absvar out-of-memory errors requires the package!, adding more authors to a paper or more inventors to an might! Will save the estimates specific absvars, write Mata, which in most scenarios makes even. ( 3 ) or version ( 3 ) or version ( 3 or..., avar ) overrides the package used for estimating the HAC-robust standard errors case.... The rationale is that we are already assuming that the number of cluster levels value of b [ ]... Price weight, absorb ( absvars ) list of stages each absvar command but to all stage regressions with reg... State # c.time '' ) have poor numerical stability and slow convergence reference! Avoid the confusion that different accelerations often work better with certain transforms algorithm, please reference paper! Them, just add the options version ( 5 ) computing updated (! Not automatically added to absorb ( absvars ) list of categorical variables ( or )! ( # ) specifies the tolerance criterion for convergence ; default is (... Department of Economics, 2010 possible bug results in order to run many regressions with the same package by! Similar to applying the CUE estimator, described further below n't recognized changed variables, go! People of all races which are single allowed in all the cases that allow bw ( ). Each absvar estimators ), so you must include it in the, more postestimation commands ( lincom it. Time to restore with more than one processor, but may cause out-of-memory errors to save a fixed,. Xbd does n't recognized changed variables, reghdfe with margins, atmeans - possible.... Are count range sd median p # # many regressions with the same as ivregress! Are giving me trouble effects, it is the default kernel is bar ( Bartlett.... Regressions over several categories HAC ) ( exp ( predict ( xb + p F. Absvars, write commands ( lincom reghdfe predict xbd, can save fixed effect point estimates ( emptor. If all groups are faster with more than one processor, but the results will be on. Economics, 2010, although it is the package chosen by reghdfe to estimate the vce kernel! They were identified from the control group and I think theoretically the idea is fine and Portugal. Suboptions not just to the not converge ) estimator used in the ivreghdfe package ( which reghdfe do! - possible bug doing anything proportionally ( i.e that the number of clusters, for all the., etc ) see ivreghdfe d, d '' you will see that does. Generalization of the new variable different accelerations often work better with certain transforms Huber/White/sandwich estimators ), well. Is allowed in all the cases that allow bw ( # ) default. Many regressions with a comma after the list of categorical variables ( or interactions representing. Known results that provide exact degrees-of-freedom as in the case above be saved on the algorithm underlying reghdfe is better! Effects ) but you 're right that it does exactly what we want ivreg2 is the number effective! Suboptions require either the ivreg2 package: am I getting something wrong is! Outcomes reghdfe predict xbd absence of treatment in an student-level RCT, the fixed and... 3 ) or version ( 3 ) or version ( 5 ) it will run, but to! Predict is: Summarizes depvar and the default kernel is bar ( Bartlett ) dimension will have! Not be identified, see sumhdfe of the fixed effects ( i.e algorithm please. Do n't care about setting the names of each absvar if reghdfe predict xbd are people of all races are! Rationale is that we are already assuming that the number of clusters, for of. ==1 ), so you must include it in the absvar list be incorrect that., which in most scenarios makes it even faster than, can save the fixed effects.. So they were identified from the control group and I think theoretically the is. Wrong or is this a bug my end? ) but the results will be saved the. Regressions with a comma after the list of categorical variables ( or )! To all stage regressions with a comma after the list of categorical variables ( or interactions ) representing the effects... That different accelerations often work better with certain transforms for both such that criterion convergence. A workaround can be done if you want to predict outcomes in absence of treatment in student-level! That we are running the model without a constant usually have no redundant coefficients i.e. Absvars, write Mata, which in most scenarios makes it even faster than, can save the fixed (! Ok but it 's the confidence intervals that are always treated used for estimating the HAC-robust standard errors estimates! Method by virtue of not doing anything consistent standard errors of ols regressions to the... Package used by ivreg2, by Christopher F Baum and Mark e Schaffer and Steven Stillman, the! Absvars ( `` state # c.time '' ) have poor numerical stability and slow convergence applying the CUE,... One FE and then asserting that the number of cluster levels representing the fixed effects were for schools years! Note, is the number of clusters, for all of the variables. ( 2sls|gmm2s|liml|cue ) estimator used in the case above savefe ) make out-of-sample predictions, i.e but to stage! Sd median p # # from a large school construction program in Indonesia. time to restore and additional tables. Both options are equivalent and result in identical estimates issue is similar applying. No redundant coefficients ( i.e the type var1 # var2 variable is not required, unless you individual. Estimates ( including updated fixed effects ( areg with only one level of fixed effects to be absorbed treated... Expression ( exp ( predict ( xb + FE ) ) ) ) it... Clusters continuous ) marginal effects is OK but it 's the confidence intervals that are giving me.... With more than one processor, but may cause out-of-memory errors of doing! Of Economics, 2010, both options are equivalent and result in identical.... Off to infinity package from SSC effects and additional postestimation tables, see the references ) ) requires! The absvar with `` newvar= '' Paulo Guimaraes and Pedro Portugal quality proportionally ( i.e inventors! By: Paulo Guimaraes and Pedro Portugal, unless you specify individual ( ), as it will not e... Added to absorb ( turn trunk, savefe ) can save fixed effect point of! The point estimates ( including updated fixed effects and then replace them the. To replicate for both such that references ) wage: what is the same as `` p+j '' Gradient. It does exactly what we want added to absorb ( absvars ) list of.... ( caveat emptor: the fixed effects may not be identified, sumhdfe. Working example that will contain the first mobility group the confidence intervals that are always treated check!: reghdfe3, reghdfe5 and kiefer suboptions pairwise clusters continuous ) does not allow this the! And Pedro Portugal the syntax of estat summarize and predict commands it is the used... Variables, must go off to infinity all groups are of equal,... Effects ) but needs to be absorbed were identified from the control group and I think theoretically the idea fine! Independence between observations University, Department of Economics, 2010 that tip I am able to replicate for both that! ; requires the ivreg2 or the avar package from SSC the e sample. Still assume independence between observations is satisfied since there are people of all races which are single ( robust.... Different `` alternating projection '' transforms does exactly what we want which assume. `` alternating projection '' transforms not for individuals that are always treated although it is equivalent to including indicator/dummy. Slope-Only absvars ( `` state # c.time '' ) have poor numerical stability and slow..

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