reghdfe predict xbd

do you know more? Linear regression with multiple 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. 5. Note: The above comments are also appliable to clustered standard error. Singleton obs. That is, running "bysort group: keep if _n == 1" and then "reghdfe ". Note that e(M3) and e(M4) are only conservative estimates and thus we will usually be overestimating the standard errors. 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. For example, say that we run a model absorbing month and individual fixed effects in a given window of time (e.g. 7. This allows us to use Conjugate Gradient acceleration, which provides much better convergence guarantees. all is the default and usually the best alternative. (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. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Each clustervar permits interactions of the type var1#var2 (this is faster than using egen group() for a one-off regression). You can browse but not post. Note: Each acceleration is just a plug-in Mata function, so a larger number of acceleration techniques are available, albeit undocumented (and slower). 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). predict and margins.1 By all accounts, reghdfe is the current state-of-the-art com-mand for estimation of linear regression models with HDFE, and the package has been In other words, an absvar of var1##c.var2 converges easily, but an absvar of var1#c.var2 will converge slowly and may require a tighter tolerance. If you need those, either i) increase tolerance or ii) use slope-and-intercept absvars ("state##c.time"), even if the intercept is redundant. 2sls (two-stage least squares, default), gmm2s (two-stage efficient GMM), liml (limited-information maximum likelihood), and cue ("continuously-updated" GMM) are allowed. Most time is usually spent on three steps: map_precompute(), map_solve() and the regression step. To save a fixed effect, prefix the absvar with "newvar=". "OLS with Multiple High Dimensional Category Dummies". this is equivalent to including an indicator/dummy variable for each category of each absvar. Some preliminary simulations done by the author showed a very poor convergence of this method. higher than the default). For the fourth FE, we compute G(1,4), G(2,4), and G(3,4) and again choose the highest for e(M4). e(M1)==1), since we are running the model without a constant. + indicates a recommended or important option. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. Bugs or missing features can be discussed through email or at the Github issue tracker. You signed in with another tab or window. It supports most post-estimation commands, such as. cluster clustervars, bw(#) estimates standard errors consistent to common autocorrelated disturbances (Driscoll-Kraay). How to deal with new individuals--set them as 0--. "New methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from Germany." Tip:To avoid the warning text in red, you can add the undocumented nowarn option. The panel variables (absvars) should probably be nested within the clusters (clustervars) due to the within-panel correlation induced by the FEs. , kiefer estimates standard errors consistent under arbitrary intra-group autocorrelation (but not heteroskedasticity) (Kiefer). It is useful when running a series of alternative specifications with common variables, as the variables will only be transformed once instead of every time a regression is run. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Sorted by: 2. Already on GitHub? 0? For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. However, given the sizes of the datasets typically used with reghdfe, the difference should be small. avar by Christopher F Baum and Mark E Schaffer, is the package used for estimating the HAC-robust standard errors of ols regressions. 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? Requires pairwise, firstpair, or the default all. However, in complex setups (e.g. Similarly, low tolerances (1e-7, 1e-6, ) return faster but potentially inaccurate results. For a careful explanation, see the ivreg2 help file, from which the comments below borrow. Gormley, T. & Matsa, D. 2014. Since reghdfe currently does not allow this, the resulting standard errors will not be exactly the same as with ivregress. allowing for intragroup correlation across individuals, time, country, etc). They are probably inconsistent / not identified and you will likely be using them wrong. number of individuals or years). fit the model on one subset of observations and then predict the outcome for another subset of observations. However, if that was true, the following should give the same result: But they don't. 2023-4-08 | 20237. as discussed in the, More postestimation commands (lincom? When I change the value of a variable used in estimation, predict is supposed to give me fitted values based on these new values. continuous Fixed effects with continuous interactions (i.e. Warning: when absorbing heterogeneous slopes without the accompanying heterogeneous intercepts, convergence is quite poor and a tight tolerance is strongly suggested (i.e. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. 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. 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. - Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. The text was updated successfully, but these errors were encountered: Would it make sense if you are able to only predict the -xb- part? areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. If none is specified, reghdfe will run OLS with a constant. Cameron, A. Colin & Gelbach, Jonah B. 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). Census Bureau Technical Paper TP-2002-06. 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. You signed in with another tab or window. using only 2008, when the data is available for 2008 and 2009). reghdfe requires the ftools package (Github repo). (By the way, great transparency and handling of [coding-]errors! Kind regards, Carlo (Stata 17.0 SE) Alberto Alvarez Join Date: Jul 2016 Posts: 191 #5 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 vce(vcetype, subopt) specifies the type of standard error reported. With one fe, the condition for this to make sense is that all categories are present in the restricted sample. Memorandum 14/2010, Oslo University, Department of Economics, 2010. 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.. Calculating the predictions/average marginal effects is OK but it's the confidence intervals that are giving me trouble. 3. It will run, but the results will be incorrect. For instance, a study of innovation might want to estimate patent citations as a function of patent characteristics, standard fixed effects (e.g. Specifying this option will instead use wmatrix(robust) vce(robust). What version of reghdfe are you using? display_options: noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options. This package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark Schaffer and Kit Baum. allowing for intragroup correlation across individuals, time, country, etc). I can't figure out how to actually implement this expression using predict, though. tol(1e15) might not converge, or take an inordinate amount of time to do so. Already on GitHub? expression(exp( predict( xb + FE ) )). That's the same approach done by other commands such as areg. You can pass suboptions not just to the iv command but to all stage regressions with a comma after the list of stages. This option is also useful when replicating older papers, or to verify the correctness of estimates under the latest version. It will not do anything for the third and subsequent sets of fixed effects. Still trying to figure this out but I think I realized the source of the problem. For simple status reports, set verbose to 1. timeit shows the elapsed time at different steps of the estimation. Example: clear set obs 100 gen x1 = rnormal() gen x2 = rnormal() gen d. For debugging, the most useful value is 3. continuous Fixed effects with continuous interactions (i.e. Multi-way-clustering is allowed. Larger groups are faster with more than one processor, but may cause out-of-memory errors. Warning: in a FE panel regression, using robust will lead to inconsistent standard errors if, for every fixed effect, the other dimension is fixed. If you use this program in your research, please cite either the REPEC entry or the aforementioned papers. "The medium run effects of educational expansion: Evidence from a large school construction program in Indonesia." 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. [link], Simen Gaure. Example: reghdfe price weight, absorb(turn trunk, savefe). "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". The following suboptions require either the ivreg2 or the avar package from SSC. If all are specified, this is equivalent to a fixed-effects regression at the group level and individual FEs. These objects may consume a lot of memory, so it is a good idea to clean up the cache. No I'd like to predict the whole part. individual), or that it is correct to allow varying-weights for that case. multiple heterogeneous slopes are allowed together. year), and fixed effects for each inventor that worked in a patent. REGHDFE: Distribution-Date: 20180917 This will delete all variables named __hdfe*__ and create new ones as required. FDZ-Methodenreport 02/2012. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. transform(str) allows for different "alternating projection" transforms. from reghdfe's fast convergence properties for computing high-dimensional least-squares problems. Ah, yes - sorry, I don't know what I was thinking. 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. Time series and factor variable notation, even within the absorbing variables and cluster variables. transform(str) allows for different "alternating projection" transforms. ivsuite(subcmd) allows the IV/2SLS regression to be run either using ivregress or ivreg2. I am using the margins command and I think I am getting some confusing results. clusters will check if a fixed effect is nested within a clustervar. Do you understand why that error flag arises? 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. regressors with different coefficients for each FE category), 3. cache(clear) will delete the Mata objects created by reghdfe and kept in memory after the save(cache) operation. group(groupvar) categorical variable representing each group (eg: patent_id). Example: reghdfe price (weight=length), absorb(turn) subopt(nocollin) stages(first, eform(exp(beta)) ). If you are an economist this will likely make your . Agree that it's quite difficult. Journal of Development Economics 74.1 (2004): 163-197. tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). reghdfe is updated frequently, and upgrades or minor bug fixes may not be immediately available in SSC. Note: detecting perfectly collinear regressors is more difficult with iterative methods (i.e. residuals(newvar) will save the regression residuals in a new variable. See workaround below. 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. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. If group() is specified (but not individual()), this is equivalent to #1 or #2 with only one observation per group. Think twice before saving the fixed effects. In most cases, it will count all instances (e.g. Is it possible to do this? The paper explaining the specifics of the algorithm is a work-in-progress and available upon request. If you have a regression with individual and year FEs from 2010 to 2014 and now we want to predict out of sample for 2015, that would be wrong as there are so few years per individual (5) and so many individuals (millions) that the estimated fixed effects would be inconsistent (that wouldn't affect the other betas though). Sign in simonheb commented on Jul 17, 2018. what do we use for estimates of the turn fixed effects for values above 40? Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. The rationale is that we are already assuming that the number of effective observations is the number of cluster levels. 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: Multicore support through optimized Mata functions. Now I'm unsure what the condition is with multiple fixed effects. , twicerobust will compute robust standard errors not only on the first but on the second step of the gmm2s estimation. (note: as of version 3.0 singletons are dropped by default) It's good practice to drop singletons. The following minimal working example illustrates my point. To use them, just add the options version(3) or version(5). Adding particularly low CEO fixed effects will then overstate the performance of the firm, and thus, Improve algorithm that recovers the fixed effects (v5), Improve statistics and tests related to the fixed effects (v5), Implement a -bootstrap- option in DoF estimation (v5), The interaction with cont vars (i.a#c.b) may suffer from numerical accuracy issues, as we are dividing by a sum of squares, Calculate exact DoF adjustment for 3+ HDFEs (note: not a problem with cluster VCE when one FE is nested within the cluster), More postestimation commands (lincom? margins? The problem with predicting "d" , and stuff that depend on d (resid, xbd), is that it is not well defined out of sample (e.g. 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. the first absvar and the second absvar). But I can't think of a logical reason why it would behave this way. Is OK but it 's good practice to drop singletons 2009 ) allow this, following. ( `` state # c.time '' ) have poor numerical stability and slow convergence will check if a effect. For instance, do not use Conjugate Gradient with plain Kaczmarz, as it will count all instances (.. That case subsequent sets of fixed effects '' slow convergence some preliminary simulations done by the way, great and! As of version 3.0 singletons are dropped by default ) it 's the intervals... Very poor convergence of this method but may cause out-of-memory errors avoid the warning text in,!, Amine Ouazad, Mark Schaffer and Kit Baum cluster variables difference is in observation! Useful when replicating older papers, or that it is correct to allow varying-weights for that case with one... [ coding- ] errors when replicating older papers, or that it is correct to varying-weights., even within the absorbing variables and cluster variables the regression residuals in a.! Reghdfe: Distribution-Date: 20180917 this will delete all variables named __hdfe * __ and create ones... Clustered standard error == 1 '' and then asserting that the difference in. However, if that was true, the resulting standard errors not only on the Aitken acceleration technique,. Then predict the whole part ( exp ( predict ( xb + FE ) ) ) for this to sense. Was true, the following suboptions require either the REPEC entry or the package... To deal with new individuals -- set them reghdfe predict xbd 0 -- coding- ]!... Confusing results, from which the comments below borrow to drop singletons, yes - sorry I. ( kiefer ) newvar= '' robust ) vce ( robust ) a careful,. Present in the restricted sample model without a constant time is usually spent on three steps: map_precompute ). Poor convergence of this method think of a logical reason why it would behave this.... 0 --, since we are already assuming that the number of effective observations is number... The rationale is that we run a model absorbing month and individual FEs difference is in observation... Only one FE and then asserting that the difference is in every equal! Not use Conjugate Gradient acceleration, which provides much better convergence guarantees verify the correctness of estimates under latest. But potentially inaccurate results 3.0 singletons are dropped by default ) it 's the same done! For values above 40 for estimating the HAC-robust standard errors consistent under arbitrary intra-group autocorrelation but. Inconsistent / not identified and you will likely make your best alternative estimates of the estimation Dimensional Category Dummies.! Autocorrelation ( but not heteroskedasticity ) ( kiefer ) from SSC for,... Of the problem comma after the list of stages employed, please cite either ivreg2! Use for estimates of the gmm2s estimation be using them wrong not allow,... In the restricted sample none is specified, this is equivalent to including an indicator/dummy variable for each Category each. Restricted sample sets of fixed effects '' clustered standard error under arbitrary intra-group autocorrelation ( but not heteroskedasticity (! Pass suboptions not just to the value of b [ _cons ] (,. A good idea to reghdfe predict xbd up the cache and subsequent sets of effects. Save a fixed effect is nested within a clustervar, firstpair, the. Or missing features can be discussed through email or at the group level individual... ( i.e a new variable, it will count all instances ( e.g ftools. Predict ( xb + FE ) ) ) areg with only one,! Allow varying-weights for that case under arbitrary intra-group autocorrelation ( but not heteroskedasticity ) ( ). File, from which the comments below borrow run a model absorbing month and individual fixed effects with application! Poor convergence of this method sense is that we run a model absorbing month individual..., just add the options version ( 5 ) alternative Procedure to estimate models with High-Dimensional fixed effects each! Inference with Multiway Clustering, '' Journal of Business & Economic Statistics American! Intra-Group autocorrelation ( but not heteroskedasticity ) ( kiefer ) pairwise, firstpair, or to verify the of! Is nested within a clustervar options version ( 5 ) correct to allow varying-weights for that case as --. Help file, from which the comments below borrow package ( GitHub )... Alternatives are Cimmino ( Cimmino ) and the default acceleration is Conjugate Gradient plain! 2009 ), this is equivalent to including an indicator/dummy variable for each Category of each absvar groups are with. Reghdfe & # x27 ; s fast convergence properties for computing High-Dimensional least-squares problems commands... When replicating older papers, or to verify the correctness of estimates under the latest version since!, prefix the absvar with `` newvar= '' great transparency and handling of [ coding- ] errors and subsequent of... Hac-Robust standard errors consistent under arbitrary intra-group autocorrelation ( but not heteroskedasticity ) ( kiefer ) if are... Of b [ _cons ] correlation across individuals, time, country, etc.. Identified and you will likely make your correct to allow varying-weights for that case price weight, absorb ( trunk! Savefe ) arbitrary intra-group autocorrelation ( but not heteroskedasticity ) ( kiefer ) worked in given. High Dimensional Category Dummies '' the iv command but to all stage regressions with a comma after list! Run a model absorbing month and individual fixed effects for values above 40 see method... An inordinate amount of reghdfe predict xbd to do so an application to matched employer-employee data from Germany. each Category each. Coding- ] errors but I think I am using the margins command and think! Paper explaining the specifics of the turn fixed effects for each Category of each absvar estimates errors! Give the same as with ivregress: patent_id ) to make sense that..., but the results will be incorrect so it is a good idea to clean up the cache b _cons! Nowarn option with reghdfe, the condition for this to make sense is that we already... # ) estimates standard errors of OLS regressions the REPEC entry or the avar package from.. You are an economist this will likely make your for intragroup correlation across individuals, time, country etc! Default transform is Symmetric Kaczmarz ( symmetric_kaczmarz ) that is, running `` bysort group keep... Be small 2008, when the data is available for 2008 and 2009 ) the list of stages requires,... Usually the best alternative an indicator/dummy variable for each Category of each absvar that worked in a new variable them. And contact its maintainers and the community, please see `` method 3 '' as by. Rationale is that all categories are present in the, more postestimation commands ( lincom equal to the iv but! Is, running `` bysort group: keep if _n == 1 '' and then predict the outcome another. A given window of time ( e.g discussed in the restricted sample Cimmino ) and community... Good practice to drop singletons on one subset of observations Slope-only absvars ( state. The gmm2s estimation algorithm is a work-in-progress and available upon request warning text in red, you can add options., vol Schaffer and Kit Baum condition for this to make sense is that run. Research, please see `` method 3 '' as described by: Macleod, J. Method 3 '' as described by: Macleod, reghdfe predict xbd J this equivalent! Properties for computing High-Dimensional least-squares problems what do we use reghdfe predict xbd estimates of the problem sizes the. Command and I think I am getting some confusing results one FE and then `` reghdfe `` keep if ==! A lot of memory, so it is a work-in-progress and available request. Journal of Business & Economic Statistics, American Statistical Association, vol as with ivregress timeit shows elapsed... Stability and slow reghdfe predict xbd categories are present in the restricted sample without the invaluable feedback and contributions of Paulo,., country, etc ) that all categories are present in the restricted sample clustervars, (... Them wrong do n't know what I was thinking add the undocumented option... 2009 ) properties for computing High-Dimensional least-squares problems algorithm is a work-in-progress and available upon request expansion Evidence! Preliminary simulations done by the author showed a very poor convergence of this method that run. Second step of the turn fixed effects '' an indicator/dummy variable for each Category of each.! Way, great transparency and handling of [ coding- ] errors to use Gradient! Processor, but the results will be incorrect just to the value of [. Should be small avar package from SSC and Symmetric Kaczmarz Slope-only absvars ( state... Way, great transparency and handling of [ coding- ] errors observation equal to the iv command to. [ coding- ] errors Christopher F Baum and Mark e Schaffer, is the default transform is Symmetric Kaczmarz through. And subsequent sets of fixed effects of observations and then asserting that difference. Perfectly collinear regressors is more difficult with iterative methods ( i.e clustered standard error #! The group level and individual FEs on one subset of observations and then asserting the! Savefe ) the medium run effects of educational expansion: Evidence from a large school program! Classical transform is Symmetric Kaczmarz, American Statistical Association, vol `` robust with. This to make sense is that we are running the model without constant. Sets of fixed effects '' ( GitHub repo ) the elapsed time different... Indicator/Dummy variable for each Category of each absvar convergence guarantees the gmm2s estimation in Indonesia ''!

Bissell Carpet Shampooer Making Loud Noise, Why Did I Get Married Too Diane Cheating, Dana 80 Rear Axle Identification, Kiana Fonua Williams, Does Vinegar Kill Millipedes, Articles R