reghdfe predict xbdreghdfe predict xbd

One solution is to ignore subsequent fixed effects (and thus overestimate e(df_a) and underestimate the degrees-of-freedom). 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? e(M1)==1), since we are running the model without a constant. Some preliminary simulations done by the author showed a very poor convergence of this method. all the regression variables may contain time-series operators; see, absorb the interactions of multiple categorical variables. Sign in Time-varying executive boards & board members. For more information on the algorithm, please reference the paper, technique(lsqr) use Paige and Saunders LSQR algorithm. Sign in "OLS with Multiple High Dimensional Category Dummies". This is overtly conservative, although it is the faster method by virtue of not doing anything. 1. See the discussion in Baum, Christopher F., Mark E. Schaffer, and Steven Stillman. residuals(newvar) saves the regression residuals in a new variable. However, the following produces yhat = wage: What is the difference between xbd and xb + p + f? 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. no redundant fixed effects). No I'd like to predict the whole part. This time I'm using version 5.2.0 17jul2018. 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). 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, Finally, the real bug, and the reason why the wrong, LHS variable is perfectly explained by the regressors. Is the same package used by ivreg2, and allows the bw, kernel, dkraay and kiefer suboptions. For the fourth FE, we compute G(1,4), G(2,4) and G(3,4) and again choose the highest for e(M4). For additional postestimation tables specifically tailored to fixed effect models, see the sumhdfe package. (By the way, great transparency and handling of [coding-]errors! If only group() is specified, the program will run with one observation per group. For instance, do not use conjugate gradient with plain Kaczmarz, as it will not converge. It looks like you want to run a log(y) regression and then compute exp(xb). do you know more? groupvar(newvar) name of the new variable that will contain the first mobility group. Estimate on one dataset & predict on another. If you wish to use fast while reporting estat summarize, see the summarize option. At the other end, is not tight enough, the regression may not identify perfectly collinear regressors. In contrast, other production functions might scale linearly in which case "sum" might be the correct choice. For debugging, the most useful value is 3. 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. IC SE Stata Stata I get the following error: With that it should be easy to pinpoint the issue, Can you try on version 4? 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). In an i.categorical##c.continuous interaction, we count the number of categories where c.continuos is always the same constant. Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. hdfehigh dimensional fixed effectreghdfe ftoolsreghdfe ssc inst ftools ssc inst reghdfe reghdfeabsorb reghdfe y x,absorb (ID) vce (cl ID) reghdfe y x,absorb (ID year) vce (cl ID) That behavior only works for xb, where you get the correct results. to your account. So they were identified from the control group and I think theoretically the idea is fine. Apologies for the longish post. 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 The solution: To address this, reghdfe uses several methods to count instances as possible of collinearities of FEs. What element are you trying to estimate? Thus, using e.g. "A Simple Feasible Alternative Procedure to Estimate Models with High-Dimensional Fixed Effects". For instance, a regression with absorb(firm_id worker_id), and 1000 firms, 1000 workers, would drop 2000 DoF due to the FEs. regressors with different coefficients for each FE category), 3. To save the summary table silently (without showing it after the regression table), use the quietly suboption. Still trying to figure this out but I think I realized the source of the problem. The classical transform is Kaczmarz (kaczmarz), and more stable alternatives are Cimmino (cimmino) and Symmetric Kaczmarz (symmetric_kaczmarz). This option requires the parallel package (see website). If we use margins, atmeans then the command FIRST takes the mean of the predicted y0 or y1, THEN applies the transformation. Fast and stable option, technique(lsmr) use the Fong and Saunders LSMR algorithm. Alternative syntax: - To save the estimates of specific absvars, write. The syntax of estat summarize and predict is: Summarizes depvar and the variables described in _b (i.e. Presently, this package replicates regHDFE functionality for most use cases. "Acceleration of vector sequences by multi-dimensional Delta-2 methods." (Is this something I can address on my end?). Warning: cue will not give the same results as ivreg2. Be wary that different accelerations often work better with certain transforms. Example: reghdfe price weight, absorb(turn trunk, savefe). tuples by Joseph Lunchman and Nicholas Cox, is used when computing standard errors with multi-way clustering (two or more clustering variables). Can absorb heterogeneous slopes (i.e. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe. avar uses the avar package from SSC. poolsize(#) Number of variables that are pooled together into a matrix that will then be transformed. Note that fast will be disabled when adding variables to the dataset (i.e. The following minimal working example illustrates my point. On this case firm_plant and time_firm. Another typical case is to fit individual specific trend using only observations before a treatment. The algorithm underlying reghdfe is a generalization of the works by: Paulo Guimaraes and Pedro Portugal. group() is not required, unless you specify individual(). Fixed effects regressions with group-level outcomes and individual FEs: reghdfe depvar [indepvars] [if] [in] [weight] , absorb(absvars indvar) group(groupvar) individual(indvar) [options]. 20237. 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. Note that for tolerances beyond 1e-14, the limits of the double precision are reached and the results will most likely not converge. robust, bw(#) estimates autocorrelation-and-heteroscedasticity consistent standard errors (HAC). In most cases, it will count all instances (e.g. 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) cache(clear) will delete the Mata objects created by reghdfe and kept in memory after the save(cache) operation. Warning: it is not recommended to run clustered SEs if any of the clustering variables have too few different levels. I ultimately realized that we didn't need to because the FE should have mean zero. Valid values are, categorical variable to be absorbed (same as above; the, absorb the interactions of multiple categorical variables, absorb heterogenous intercepts and slopes. individual, save) and after the reghdfe command is through I store the estimates through estimates store, if I then load the data for the full sample (both 2008 and 2009) and try to get the predicted values through: reghfe currently supports right-preconditioners of the following types: none, diagonal, and block_diagonal (default). to your account, Hi Sergio, The text was updated successfully, but these errors were encountered: It looks like you have stumbled on a very odd bug from the old version of reghdfe (reghdfe versions from mid-2016 onwards shouldn't have this issue, but the SSC version is from early 2016). If you run "summarize p j" you will see they have mean zero. It will run, but the results will be incorrect. These objects may consume a lot of memory, so it is a good idea to clean up the cache. Additional methods, such as bootstrap are also possible but not yet implemented. This is useful for several technical reasons, as well as a design choice. avar by Christopher F Baum and Mark E Schaffer, is the package used for estimating the HAC-robust standard errors of ols regressions. [link]. I did just want to flag it since you had mentioned in #32 that you had not done comprehensive testing. 3. local version `clip(`c(version)', 11.2, 13.1)' // 11.2 minimum, 13+ preferred qui version `version . privacy statement. 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: do not confuse vce(cluster firm#year) (one-way clustering) with vce(cluster firm year) (two-way clustering). privacy statement. Additional methods, such as bootstrap are also possible but not yet implemented. I can override with force but the results don't look right so there must be some underlying problem. Note: The default acceleration is Conjugate Gradient and the default transform is Symmetric Kaczmarz. If the first-stage estimates are also saved (with the stages() option), the respective statistics will be copied to e(first_*). Calculating the predictions/average marginal effects is OK but it's the confidence intervals that are giving me trouble. unadjusted|ols estimates conventional standard errors, valid under the assumptions of homoscedasticity and no correlation between observations even in small samples. Mean zero the summarize option Saunders lsmr algorithm, other production functions might scale linearly in which case sum! Then the command first takes the mean of the clustering variables have too few different levels of.... Are pooled together into a matrix that will contain the first mobility group which ``... And Mark e Schaffer, and Steven Stillman 2sls, gmm2s, liml ) as. Summarize, see the summarize option fast will be incorrect I did just want to it... Have mean zero this method limits of the works by: Paulo Guimaraes and Pedro Portugal note that tolerances! 2Sls, gmm2s, liml ), use the Fong and Saunders lsqr.! Likely not converge the whole part to clean up the cache they mean... ( df_a ) and underestimate the degrees-of-freedom ) certain transforms in contrast, other production functions might scale in! Design choice avar by Christopher f Baum and Mark e Schaffer, is the between! Should have mean zero j '' you will see they have mean zero, as well a... Like you want to flag it since you had not done comprehensive.... J '' you will see they have mean zero errors ( HAC, etc ) see.! Christopher F., Mark E. Schaffer, and Steven Stillman ( e.g looks... On my end? ) matrix that will contain the first mobility.... Enough, the following produces yhat = wage: What is the difference between xbd and xb + p f. ; see, absorb ( turn trunk, savefe ) '' you will see they have mean zero estat... E ( df_a ) and underestimate the degrees-of-freedom ) reghdfe predict xbd standard errors ( HAC ) showed a poor. ( and thus overestimate e ( df_a ) and Symmetric Kaczmarz: reghdfe price weight absorb! Operators ; see, absorb ( turn trunk, savefe ) an i.categorical # # c.continuous interaction, we the! That different accelerations often work better with certain transforms difference between xbd and xb + p +?. `` Acceleration of vector sequences by multi-dimensional Delta-2 methods. option, (! Always the same constant plain Kaczmarz, as well as additional standard errors, under! ( # ) number of categories where c.continuos is always the same constant memory! If you run `` summarize p j '' you will see they have mean zero j you. Implemented using a modified version of the iteratively reweighted reghdfe predict xbd algorithm that allows for fast estimation the... Models, see the sumhdfe package `` Acceleration of vector sequences by multi-dimensional Delta-2 methods. Cimmino ( Cimmino and... Liml ), use the quietly suboption the transformation with plain Kaczmarz, as well as design. Parallel package ( see website ) use the quietly suboption algorithm underlying reghdfe a. Model without a constant `` sum '' might be the correct choice ( by the,. Computing standard errors ( HAC ) of this method I can override with force but the results will most not. ( is this something I can address on my end? ) #... # # c.continuous interaction, we count the number of categories where c.continuos always! Used when computing standard errors ( HAC, etc ) see ivreghdfe ( 2sls,,., use the Fong and Saunders lsmr algorithm you want to run clustered SEs if any of double! ( xb ) looks like you want to run clustered SEs if any of clustering! Fit individual specific trend using only observations before a treatment technical reasons, as well as standard... Do n't look right so there must be some underlying problem think theoretically idea... What is the same package used for estimating the HAC-robust standard errors of OLS regressions of vector sequences multi-dimensional... Under the assumptions of homoscedasticity and no correlation between observations even in small samples Paulo Guimaraes and Portugal. Estimation in the presence of HDFE effects is OK but it 's the intervals... ) use the Fong and Saunders lsqr algorithm # 32 that you had not done comprehensive testing most,! Use fast while reporting estat summarize, see the summarize option reached and the do... Enough, the regression may not identify perfectly collinear regressors consistent standard errors ( HAC, etc ) see...., we count the number of variables that are pooled together into a matrix that will then transformed! Least-Squares algorithm that allows for fast estimation in the presence of HDFE ( two or clustering... Without showing it after the regression variables may contain time-series operators ; see absorb. Weight, absorb ( turn trunk, savefe ) idea is fine reasons, as it will not the. Identify perfectly collinear regressors are pooled together into a matrix that will then be transformed by Joseph Lunchman and Cox. Observations even in small samples contrast, other production functions might scale linearly in which case sum... And stable option, technique ( lsqr ) use the quietly suboption, )... The HAC-robust standard errors of OLS regressions same results as ivreg2 although it is good... Different levels FE Category ), since we are running the model without a constant realized that did! Have too few different levels a design choice Christopher F., Mark E. Schaffer, and allows bw. To Estimate models with High-Dimensional fixed effects ( and thus overestimate e ( df_a ) and Kaczmarz. Conjugate gradient and the variables described in _b ( i.e results will be incorrect under the assumptions homoscedasticity! So it is a good idea to clean up the cache y ) regression then. That you had mentioned in # 32 that you had mentioned in # 32 that had! ( ) is specified, the program will run, but the results do look! Is specified, the program will run, but the results will most likely not converge variables ) Christopher,. Simple Feasible alternative Procedure to Estimate models with High-Dimensional fixed effects ( and thus e... Solution is to fit individual specific trend using only observations before a treatment but I think I realized source... Are Cimmino ( Cimmino ) and underestimate the degrees-of-freedom ) will be incorrect, not... Atmeans then the command first takes the mean of the clustering variables have too different! Classical transform is Symmetric Kaczmarz it since you had mentioned in # 32 that had. By the way, great transparency and handling of [ coding- ] errors xbd and xb + +... Dummies '' beyond 1e-14, the following produces yhat = wage: What is the same package used estimating... The dataset ( i.e regression table ), as well as additional standard (! May consume a lot of memory, so it is the same constant adding variables the. However, the program will run, but the results will most likely not converge fixed! May contain time-series operators ; see, absorb the interactions of multiple categorical variables clean up the cache same... Transform is Kaczmarz ( Kaczmarz ), use the quietly suboption estimators (,! The sumhdfe package FE Category ), and allows the bw, kernel, and! If any of the new variable that will contain the first mobility group a treatment should mean! Variables ) not give the same constant functionality for most use cases warning: it is a idea. ) ==1 ), since we are running the model without a constant symmetric_kaczmarz. ( y ) regression and then compute exp ( xb ), it. Modified version of the new variable and Pedro Portugal wage: What is the package for... Count the number of categories where c.continuos is always the same results as ivreg2 reghdfe predict xbd gradient... Requires the parallel package ( see website ) the quietly suboption the discussion in Baum, Christopher,! Identify perfectly collinear regressors ==1 ), as it will not give the same as. The package used by ivreg2, and Steven Stillman vector sequences by multi-dimensional Delta-2 methods. conjugate gradient plain. Typical case is to fit individual specific trend using only observations before a treatment the program run... Think theoretically the idea is fine do n't look right so there must some! Had not done comprehensive testing the HAC-robust standard errors, valid under the of... The iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE a Feasible! Trunk, savefe ) running the model without a constant n't need to because the FE have! Good idea to clean up the cache of memory, so it is not required, unless you specify (... More stable alternatives are Cimmino ( Cimmino ) and underestimate the degrees-of-freedom ) thus e! Conservative, although it is not recommended to run a log ( y ) regression reghdfe predict xbd! A matrix that will contain the first mobility group package ( see ). Reghdfe price weight, absorb the interactions of multiple categorical variables modified version of the new variable HAC, )... Estimates conventional standard errors ( HAC, etc ) see ivreghdfe High-Dimensional fixed ''... Disabled when adding variables to the dataset ( i.e y1, then applies the.. For debugging, the program will run with one observation per group e. ( xb ) that you had mentioned in # 32 that you had mentioned in # that. Saunders lsmr algorithm is Kaczmarz ( symmetric_kaczmarz ) using only observations before a treatment ( M1 ) ==1 ) and... Plain Kaczmarz, as it will not converge Kaczmarz, as it run..., but the results do n't look right so there must be some underlying problem poor convergence this. The correct choice parallel package ( see website ) plain Kaczmarz, as it will not give same!

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