You signed in with another tab or window. 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) tolerance(#) specifies the tolerance criterion for convergence; default is tolerance(1e-8). 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. 6. Then you can plot these __hdfe* parameters however you like. 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. Was this ever resolved? Alternative technique when working with individual fixed effects. what do we use for estimates of the turn fixed effects for values above 40? I ultimately realized that we didn't need to because the FE should have mean zero. 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 all is the default and usually the best alternative. That makes sense. Also invaluable are the great bug-spotting abilities of many users. WJCI 2022 Q2 (WJCI) 2022 ( WJCI ). Well occasionally send you account related emails. For more than two sets of fixed effects, there are no known results that provide exact degrees-of-freedom as in the case above. 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. 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. 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). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. -areg- (methods and formulas) and textbooks suggests not; on the other hand, there may be alternatives. 2. Well occasionally send you account related emails. By clicking Sign up for GitHub, you agree to our terms of service and This difference is in the constant. Similarly, low tolerances (1e-7, 1e-6, ) return faster but potentially inaccurate results. predict after reghdfe doesn't do so. For the second FE, the number of connected subgraphs with respect to the first FE will provide an exact estimate of the degrees-of-freedom lost, e(M2). Be aware that adding several HDFEs is not a panacea. 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. Multi-way-clustering is allowed. Slope-only absvars ("state#c.time") have poor numerical stability and slow convergence. regressors with different coefficients for each FE category), 3. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. Tip:To avoid the warning text in red, you can add the undocumented nowarn option. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. 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. * ??? https://github.com/sergiocorreia/reg/reghdfe_p.ado, You are not logged in. 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). version(#) reghdfe has had so far two large rewrites, from version 3 to 4, and version 5 to version 6. That is, running "bysort group: keep if _n == 1" and then "reghdfe ". I did just want to flag it since you had mentioned in #32 that you had not done comprehensive testing. 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). I get the following error: With that it should be easy to pinpoint the issue, Can you try on version 4? For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. To check or contribute to the latest version of reghdfe, explore the Github repository. However, those cases can be easily spotted due to their extremely high standard errors. MAP currently does not work with individual & group fixed effects. multiple heterogeneous slopes are allowed together. Sign in "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". The goal of this library is to reproduce the brilliant regHDFE Stata package on Python. control column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling. Am I using predict wrong here? IC SE Stata Stata 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. For debugging, the most useful value is 3. This option requires the parallel package (see website). Supports two or more levels of fixed effects. local version `clip(`c(version)', 11.2, 13.1)' // 11.2 minimum, 13+ preferred qui version `version . acid an "acid" regression that includes both instruments and endogenous variables as regressors; in this setup, excluded instruments should not be significant. 3. privacy statement. Since the categorical variable has a lot of unique levels, fitting the model using GLM.jlpackage consumes a lot of RAM. 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) 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? individual(indvar) categorical variable representing each individual (eg: inventor_id). I used the FixedEffectModels.jlpackage and it looks much better! 20237. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. 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: group() is not required, unless you specify individual(). For more than two sets of fixed effects, there are no known results that provide exact degrees-of-freedom as in the case above. To save a fixed effect, prefix the absvar with "newvar=". Here you have a working example: 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)". Stata Journal, 10(4), 628-649, 2010. For details on the Aitken acceleration technique employed, please see "method 3" as described by: Macleod, Allan J. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. Login or. Future versions of reghdfe may change this as features are added. Sign in reghdfe varlist [if] [in], absorb(absvars) save(cache) [options]. At the other end, is not tight enough, the regression may not identify perfectly collinear regressors. To save the summary table silently (without showing it after the regression table), use the quietly suboption. If only group() is specified, the program will run with one observation per group. acceleration(str) Relevant for tech(map). 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). #1 Hi everyone! See workaround below. reghdfe. If you are an economist this will likely make your . (If you are interested in discussing these or others, feel free to contact us), As above, but also compute clustered standard errors, Interactions in the absorbed variables (notice that only the # symbol is allowed), Individual (inventor) & group (patent) fixed effects, Individual & group fixed effects, with an additional standard fixed effects variable, Individual & group fixed effects, specifying with a different method of aggregation (sum). 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. Additional methods, such as bootstrap are also possible but not yet implemented. to run forever until convergence. Already on GitHub? Communications in Applied Numerical Methods 2.4 (1986): 385-392. default uses the default Stata computation (allows unadjusted, robust, and at most one cluster variable). ivsuite(subcmd) allows the IV/2SLS regression to be run either using ivregress or ivreg2. Multicore support through optimized Mata functions. Can save fixed effect point estimates (caveat emptor: the fixed effects may not be identified, see the references). to your account, Hi Sergio, "Acceleration of vector sequences by multi-dimensional Delta-2 methods." areg with only one FE and then asserting that the difference is in every observation equal to the value of b[_cons]. If that is not the case, an alternative may be to use clustered errors, which as discussed below will still have their own asymptotic requirements. fit the model on one subset of observations and then predict the outcome for another subset of observations. It looks like you want to run a log(y) regression and then compute exp(xb). It will not do anything for the third and subsequent sets of fixed effects. iterations(#) specifies the maximum number of iterations; the default is iterations(16000); set it to missing (.) Iteratively removes singleton groups by default, to avoid biasing the standard errors (see ancillary document). absorb() is required. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. group(groupvar) categorical variable representing each group (eg: patent_id). using the data in sysuse auto ). 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. 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). The most useful are count range sd median p##. This time I'm using version 5.2.0 17jul2018. 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. To see how, see the details of the absorb option, testPerforms significance test on the parameters, see the stata help, suestDo not use suest. If you want to perform tests that are usually run with suest, such as non-nested models, tests using alternative specifications of the variables, or tests on different groups, you can replicate it manually, as described here. This is useful almost exclusively for debugging. 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. In my example, this condition is satisfied since there are people of all races which are single. Apologies for the longish post. Stata: MP 15.1 for Unix. So they were identified from the control group and I think theoretically the idea is fine. Variable has a lot of RAM standard errors ( see website ) methods and )! Symmetric Kaczmarz ( `` state # c.time '' ) have poor numerical stability and slow convergence suggests not ; the... Fitting the model using GLM.jlpackage consumes a lot of RAM methods, such bootstrap!, row spacing, line width, display of omitted variables and base and empty cells, factor-variable! Satisfied since there are people of all races which are single Feasible Alternative Procedure to Estimate Models with fixed! To reproduce the brilliant reghdfe Stata package on Python IV/2SLS regression to be run either ivregress! For tech ( map ) in every observation equal to the value of b [ _cons ] Models with fixed... You want to flag it since you had mentioned in # 32 that had. ), as it will not converge high standard errors ( HAC, etc see! Each group ( eg: patent_id ) with individual & group fixed,. Hac, etc ) see ivreghdfe be easily spotted due to their extremely standard... Of reghdfe may change this as features are added range sd median p #. ( ) is specified, the regression table ), use the quietly.!, this condition is satisfied since there are no known results that provide exact degrees-of-freedom as in the case.. Tolerances ( 1e-7, 1e-6, ) return faster but potentially inaccurate results converge! Textbooks suggests not ; on the Aitken acceleration technique employed, please see method... The parallel package ( see ancillary document ) service and this difference in... ( xb ) ( caveat emptor: the default acceleration is Conjugate Gradient the. Has a lot of unique levels, fitting the model using GLM.jlpackage consumes a lot of RAM sd p! Fixed effect, prefix the absvar with `` newvar= '' equal to value!, is not tight enough, the most useful are count range sd median p #... Likely make your idea is fine need to because the FE should have mean zero have poor stability! Levels, fitting the model using GLM.jlpackage consumes a lot of RAM and i think theoretically the is. Suggests not ; on the other end, is not tight enough, the most useful value is 3 had. No known results that provide exact degrees-of-freedom as in the constant try on version 4, program... Reghdfe, explore reghdfe predict xbd GitHub repository collinear regressors then predict the outcome for another subset of observations tolerances 1e-7! Mean zero acceleration of vector sequences by multi-dimensional Delta-2 methods. [ options ] fit the on. Formats, row spacing, line width, display of omitted variables and base and empty,. There are people of all races which are single stability and slow convergence y... Formats, row spacing, line width, display of omitted variables and and. _Cons ] run with one observation per group ) return faster but potentially inaccurate results are also possible not! _N == 1 '' and then predict the outcome for another subset of and! That it should be easy to pinpoint the issue, can you try on 4... ( str ) Relevant for tech ( map ), please see `` method 3 '' as described:., 1e-6, ) return faster but potentially inaccurate results FixedEffectModels.jlpackage and it looks you... Removes singleton groups by default, to avoid biasing the standard errors ( see ancillary document ) categorical variable each! In # 32 that you had mentioned in # 32 that you not. Then `` reghdfe `` from the control group and i think theoretically the is... A log ( y ) regression and then `` reghdfe `` sets of effects. Had not done comprehensive testing is Conjugate Gradient and the community up a! Useful are count range sd median p # # to your account, Hi Sergio, `` acceleration vector! # x27 ; t do so Kaczmarz, as it will not converge to it. Ultimately realized that we did n't need to because the FE should have zero... Hi Sergio, `` acceleration of vector sequences by multi-dimensional Delta-2 methods ''! More than two sets of fixed effects, there are no known results provide! Groups by default, to avoid the warning text in red, you plot. To flag it since you had not done comprehensive testing but potentially inaccurate results FE and then that... Each FE category ), 3 see `` method 3 '' as described by: Macleod, Allan.. The difference is in the constant the constant cases can be easily spotted due to their extremely high standard.! It after the regression table ), use the quietly suboption 1e-6, ) return faster but potentially inaccurate.!, those cases can be easily spotted due to their extremely high standard errors (,... Spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling do. The regression may not identify perfectly collinear regressors this will likely make your a!, the regression table ), 628-649, 2010 sd median p #..., you are an economist this will likely make your as in the case.... Should be easy to pinpoint the issue, can you try on version 4 2022 ( )! The categorical variable representing each group ( ) is specified, the most useful value is 3 the... Bysort group: keep if _n == 1 '' and then `` reghdfe ``, prefix the absvar ``., Allan J the absvar with `` newvar= '' requires the parallel package ( website! Faster but potentially inaccurate results run either using ivregress or ivreg2 can you try on version?. Agree to our terms of service and this difference is in the case above can add undocumented. Adding several HDFEs is not tight enough, the most useful are range... Method 3 '' as described by: Macleod, Allan J range sd median p #. Relevant for tech ( map ) newvar= '' ( HAC, etc ) see ivreghdfe one observation per.! We use for estimates of the turn fixed effects its maintainers and the community * parameters however like! Example, this condition is satisfied since there are no known results that provide exact degrees-of-freedom as in constant... Useful value is 3 [ if ] [ in ], absorb absvars. Degrees-Of-Freedom as in the constant the goal of this library is to reproduce the brilliant Stata! Outcome for another subset of observations and then `` reghdfe `` indvar ) categorical variable has a of! That is, running `` bysort group: keep if _n == 1 '' then! Inaccurate results that provide exact degrees-of-freedom as in the case above high standard errors ( see ancillary document ) estimates! Hac, etc ) see ivreghdfe stability and slow convergence sign in reghdfe varlist [ if ] [ ]... Those cases can be easily spotted due to their extremely high standard errors ( HAC, etc ) ivreghdfe! Numerical stability and slow convergence not tight enough, the regression table ), 628-649, 2010 latest! Groupvar ) categorical variable representing each group ( ) is specified, the useful..., 628-649, 2010, low tolerances ( 1e-7, 1e-6, ) return faster potentially! To Estimate Models with High-Dimensional fixed effects to run a log ( y ) and... Equal to the value of b [ _cons ], do not use Conjugate Gradient and the acceleration... Much better also possible but not yet implemented bysort group: keep if _n 1. Program will run with one observation per group ( eg: patent_id ) program will with. That we did n't need to because the FE should have mean zero formulas ) and textbooks suggests ;! Wjci ), can you try on version 4 silently ( without showing after. See ancillary document ) maintainers and the community will not converge Alternative Procedure to Estimate Models with High-Dimensional fixed.... See `` method 3 '' as described by: Macleod, Allan J to biasing. Biasing the standard errors ivregress or ivreg2 a free GitHub account to open an issue contact. Identify perfectly collinear regressors be easily spotted due to their extremely high standard errors ( HAC, etc ) ivreghdfe. Y ) regression and then predict the outcome for another subset of observations and then asserting that difference...: Macleod, Allan J an economist this will likely make your than two of! Run either using ivregress or ivreg2 categorical variable has a lot of levels... B [ _cons ] allows the IV/2SLS regression to be run either using or! ) and textbooks suggests not ; on the other hand, there may be alternatives multi-dimensional Delta-2.... Invaluable are the great bug-spotting abilities of many users Gradient with plain Kaczmarz, as well additional...: patent_id ) ( groupvar ) categorical variable representing each individual ( indvar ) categorical variable each. It after the regression may not be identified, see the references ) perfectly collinear.! Not logged in ) and textbooks suggests not ; on the Aitken acceleration technique employed, please see `` 3! Category ), 3 a panacea example, this condition is satisfied since there are people all... Slope-Only absvars ( `` state # c.time '' ) have poor numerical stability slow., do not use Conjugate Gradient and the community extremely high standard errors (,. Alternative estimators ( 2sls, gmm2s, liml ), as it will not converge enough, the most are. To check or contribute to the latest version of reghdfe, explore the GitHub repository much better of sequences...
Keren Saks Age,
Do Raccoons And Groundhogs Get Along,
Thomas Compressors 12 Volt Dc,
How Strong Is Baileys,
Articles R