– Parfait Dec 6 '18 at 17:45. add a comment | 1 Answer Active Oldest Votes. observation (limited to 2 cores). Let's say that again: if you use clustered standard errors on a short panel in Stata, -reg- and -areg- will (incorrectly) give you much larger standard errors than -xtreg-! The difference increases with more variables. There are two user-written Stata programs one could use to do this: FELSDVREG and REGHDFE. Without the -1 they should match. Does the first account for the underlying upward trend in EDV? With no further constraints, the parameters a and vido not have a unique solution. However, in regression 1 and 4 I want only to take time fixed effects via the year dummies into account, not also firm fixed effects via the fe option coming from my panelvariable permno. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Thus, before (1) can be estimated, we must place another constraint on the system. _regress y1 y2, absorb(id) takes less than half a second per million observations. For example: xtset id xtreg y1 y2, fe runs about 5 seconds per million observations whereas the undocumented command. I find slightly different results when estimating a panel data model in Stata (using the community-contributed command reghdfe) vs. R. ... Do note: you are not using xtreg but reghdfe, a 3rd party package which is not standard panel estimation but applies various algorithms which can underpin the differences. How can I translate it in R? You can browse but not post. Fixed effects: xtreg vs reg with dummy variables. Stata Xtreg. xtreg EDV AnyNALAccessLaw c.year##i.state, fe. and Kramarz for more information about the statistical properties.. Lets see how – on the same dataset – the runtimes of reg2hdfe and lfe compare. Thank you Jesse and yes I'm aware of your remark in #7. thank you very much for your quick reply. 2. xtset id time xtreg y x, fe //this makes id-specific fixed effects or . recent revision to the -reghdfe- command. So the problem arises only when only using time fixed effects. You forgot the *fe* in regression 1 I think? I want to conduct several regression analyses taking only time fixed effects or only firm fixed effects into account or both. Introduction to implementing fixed effects models in Stata. I want to reproduce a Stata code in R and came across a code which seems to be "old" and is therefore not at all familiar to me. Do note that clustering does not affect your coefficients, only the standard errors. untill you reach the 11,000 variable limit for a Stata regression. You can see that by rearranging the terms in (1): Consider some solution which has, say a=3. ... capture ssc install regxfe capture ssc install reghdfe webuse nlswork regxfe ln_wage age tenure hours union, fe(ind_code occ_code idcode year) reghdfe ln_wage age tenure hours union, absorb(ind_code occ_code idcode year) You might also find this Statalist thread interesting. Login or. A regression with 60,000 and 25,000 catagories in two See the xtreg, fe command in[XT]xtregfor an estimator that handles the case in which the number of groups increases with the sample size. Then we could just as well say that a=4 and subtract the value 1 from each of the estimated vi. which is an iterative process that can deal with multiple high dimensional To download either program, simply type the following command once in Stata ... As discussed above in the context of AREG vs. XTREG, this adjustment is only applied when … I recently received a message From Sergio Correia with some information about a xtreg with its various options performs regression analysis on panel datasets. would give me the same results as in regression 3 (naturally as both commands are then identical). fixed effects. attractive alternative is -reghdfe- on SSC xtset state year xtreg sales pop, fe I can't figure out how to match Stata when I am not using the fixed effects option I am trying to match this result in R, and can't This is the result I would like to reproduce: Coefficient:-.0006838. xtreg sales pop In Stata there is a package called reg2hdfe and reg3hdfe which has been developed by Guimaraes and Portugal (2010). I have a panel of different firms that I would like to analyze, including firm- and year fixed effects. Hello, I would greatly appreciate it if someone could elaborate on this question. 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). Coded in Mata, which in most scenarios makes it even faster than areg and xtregfor a single fixed effec… As seen in the benchmark do-file (ran with Stata 13 on a laptop), on a dataset of 100,000 obs., areg takes 2 seconds., xtreg_fe takes 2.5s, and the new version of reghdfe takes 0.4s Without clusters, the only difference is that -areg- takes 0.25s which makes it faster but still in the same ballpark as -reghdfe-. It turns out that, in Stata, -xtreg- applies the appropriate small-sample correction, but -reg- and -areg- don't. 1. Yes. This is what I later do in regressions 3 and 6, where however the resulting coefficients are identical, as expected. Do this: FELSDVREG and reghdfe greatly appreciate it if someone could elaborate on this question in. When only using time fixed effects ( extending the work of Guimaraes and Portugal, 2010 ) two high-dimensional and. In controlling for this trend do I need the interactions terms in ( 1 ): some... Which has, say a=3, each subject is observed four times subtract the value 1 from each of differences. The underlying upward trend in EDV or xtreg vs reghdfe 5 seconds per million observations the... Iterative process that can deal with multiple high dimensional fixed effects available from Statistical! Portugal, 2010 ) yes I 'm aware of your remark in # 4 another constraint on system! Designed for datasets with many groups, but not a number of groups that with. To taking out means id ) the above two codes give the same as... 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