- Current SSC version:
6.12.3 20aug2023 - Jump to:
newscitationinstallmanual installdescription
- version 6.12.4 12sep2023:
- Fix
ivreghdfebug when clustering with string variables (#276)
- Fix
- version 6.12.3 20aug2023:
- Bugfix for parallel option (macOS)
- Fix typos in help file
- version 6.12.0 26June2021:
- Add support for individual fixed effects, through new options: indiv() group() aggregation(). See Constantine and Correia (2021) as well as the help file.
- Add experimental support for parallelization via the parallel package
- Misc. code refactoring
- To use older versions of reghdfe, you can use
version(3)andversion(5). Those two are the latest versions before a major rewrite. This supercedes theoldoption.
- version 5.7.3 13nov2019:
- Fix rare error with compact option (#194). Version also submitted to SSC.
- version 5.7.0 20mar2019:
- Users no longer have to run
reghdfe, compileafter installing. If you are getting the error "class FixedEffects undefined", either upgrade to this version, or runreghdfe, compile
- Users no longer have to run
- version 5.6.8 03mar2019:
ppmlhdfepackage released, for Poisson models with fixed effects. Use this if you are running regressions withlog(y)on the left-hand-side.- Stable version of reghdfe, also on SSC.
- version 5.6.2 10feb2019:
- Minimum required version required to run Poisson pseudo-maximum likelihood estimation
- version 5.6 26jan2019:
- Improved numerical accuracy. Previously, reghdfe standardized the data, partialled it out, unstandardized it, and solved the least squares problem. It now runs the solver on the standardized data, which preserves numerical accuracy on datasets with extreme combinations of values. Thanks to Zhaojun Huang for the bug report.
- Speed up calls to reghdfe. The first call to reghdfe after "clear all" should be around 2s faster, and each subsequent call around 0.1s faster.
- Running reghdfe with
noabsorboption should now be considerably faster.
- version 5.3 30nov2018:
- Fixed silent error with Stata 15 and version 5.2.x of reghdfe. Data was loading into Mata in the incorrect order if running regressions with many factor interactions. This resulted in a scrambling of the coefficients. Stata 15 users are strongly encouraged to upgrade. For more information see #150 by @simonheb
- version 5.2 17jul2018:
- Added partial workaround for bug/quick when loading factor variables through
st_data(). This does not affect Stata 15 users (seehelp fvtrack). (Note: this speed-up has been completely disabled as of 5.3.2) - Misc. optimizations and refactoring.
- Improved support for
ppmlhdfepackage (which adds fixed effects to Poisson and other GLM models).
- Added partial workaround for bug/quick when loading factor variables through
- version 5.1 08jul2018:
- Added the
compactandpoolsize(#)options, to reduce memory usage. This can reduce reghdfe's memory usage by up to 5x-10x, at a slight speed cost. - Automatically check that the installed version of ftools is not too old.
- Added the
- version 5.0 29jun2018:
- Added support for
basevar. This is not very useful by itself but makes some postestimation packages (coefplot) easier to use - Added support for
marginspostestimation command. - Added
_consrow to output table, so the intercept is reported (as in regress/xtreg/areg). Thenoconstantoption disables this, but doing so might make the output ofmarginincorrect. predict, xbnow includes the value of_cons, which before was included inpredict, d.
- Added support for
- version 4.4 11sep2017:
- Performance: speedup when using weights, reduced memory usage, improve convergence detection
- Bugfixes:
summarizeoption was using full sample instead of regression sample, fixed a recent bug that failed to detect when FEs were nested within clusters - Mata: refactor Mata internals and add their description to
help reghdfe_mata; clean up warning messages - Poisson/PPML HDFE: extend Mata internals so we can e.g. change weights without creating an entirely new object. This is mostly to speed up the
ppmlhdfepackage.
- version 4.3 07jun2017: speed up fixed slopes (precompute
inv(xx)) - version 4.2 06apr2017: fix numerical accuracy issues (bugfixes)
- version 4.1 28feb2017: entirely rewriten in Mata
- 3-10x faster thanks to
ftoolspackage (use it if you have large datasets!) - Several minor bugs have been fixed, in particular some that did not allow complex factor variable expressions.
reghdfeis now written entirely as a Mata object. For an example of how to use it to write other programs, see here- Additional estimation options are now supported, including LSMR and pruning of degree-1 vertices.
- 3-10x faster thanks to
reghdfedepends on theftoolspackage (andboottestfor Stata 12 and older)- IV/GMM is not done directly with
reghdfebut throughivreg2. See this port, which adds anabsorb()option toivreg2. This is also useful for using more advanced standard error estimates, whichivreg2supports. - If you use commands that depend on reghdfe (
regife,poi2hdfe,ppml_panel_sg, etc.), check that they have been updated before using the new version of reghdfe. - Some options are not yet fully supported. They include
cacheandgroupvar. - The previous stable release (3.2.9 21feb2016) can be accessed with the
oldoption
- Add back group3hdfe option
reghdfe implements the estimator described in Correia (2017).
If you use it, please cite either the paper and/or the command's RePEc citation:
@TechReport {Correia2017:HDFE,
Author = {Correia, Sergio},
Title = {Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator},
Note = {Working Paper},
Year = {2017},
}Correia, Sergio. 2017. "Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator" Working Paper. http://scorreia.com/research/hdfe.pdf
Noah Constantine, Sergio Correia, 2021. reghdfe: Stata module for linear and instrumental-variable/GMM regression absorbing multiple levels of fixed effects. https://ideas.repec.org/c/boc/bocode/s457874.html
To find out which version you have installed, type reghdfe, version.
reghdfe 6.x is not yet in SSC. To quickly install it and all its dependencies, copy/paste these lines and run them:
* Install ftools (remove program if it existed previously)
cap ado uninstall ftools
net install ftools, from("https://raw.githubusercontent.com/sergiocorreia/ftools/master/src/")
* Install reghdfe 6.x
cap ado uninstall reghdfe
net install reghdfe, from("https://raw.githubusercontent.com/sergiocorreia/reghdfe/master/src/")To run IV/GMM regressions with ivreghdfe, also run these lines:
cap ado uninstall ivreg2hdfe
cap ado uninstall ivreghdfe
cap ssc install ivreg2 // Install ivreg2, the core package
net install ivreghdfe, from(https://raw.githubusercontent.com/sergiocorreia/ivreghdfe/master/src/)
Alternatively, you can install the stable/older version from SSC (5.x):
cap ado uninstall reghdfe
ssc install reghdfeTo install reghdfe to a firewalled server, you need to download these zip files by hand and extract them:
ftools(https://codeload.github.com/sergiocorreia/ftools/zip/master)reghdfe(https://codeload.github.com/sergiocorreia/reghdfe/zip/master)ivreghdfe(https://codeload.github.com/sergiocorreia/ivreghdfe/zip/master)
Then, run the following, adjusting the folder names:
cap ado uninstall ftools
cap ado uninstall reghdfe
cap ado uninstall ivreghdfe
net install ftools, from(c:\git\ftools)
net install reghdfe, from(c:\git\reghdfe)
net install ivreghdfe, from(c:\git\ivreghdfe)
Note that you can now also use Github releases in order to install specific versions.
reghdfe is a Stata package that estimates linear regressions with multiple levels of fixed effects. It works as a generalization of the built-in areg, xtreg,fe and xtivreg,fe regression commands. It's objectives are similar to the R package lfe by Simen Gaure and to the Julia package FixedEffectModels by Matthieu Gomez (beta). It's features include:
- A novel and robust algorithm that efficiently absorbs multiple fixed effects. It improves on the work by Abowd et al, 2002, Guimaraes and Portugal, 2010 and Simen Gaure, 2013. This algorithm works particularly well on "hard cases" that converge very slowly (or fail to converge) with the existing algorithms.
- Extremely fast compared to similar Stata programs.
- With one fixed effect and clustered-standard errors, it is 3-4 times faster than
aregandxtreg,fe(see benchmarks). Note: speed improvements in Stata 14 have reduced this gap. - With multiple fixed effects, it is at least an order of magnitude faster that the alternatives (
reg2hdfe,a2reg,felsdvreg,res2fe, etc.). Note: a recent paper by Somaini and Wolak, 2015 reported thatres2fewas faster thanreghdfeon some scenarios (namely, with only two fixed effects, where the second fixed effect was low-dimensional). This is no longer correct for the current version ofreghdfe, which outperformsres2feeven on the authors' benchmark (with a low-dimensional second fixed effect; see the benchmark results and the Stata code).
- With one fixed effect and clustered-standard errors, it is 3-4 times faster than
- Allows two- and multi-way clustering of standard errors, as described in Cameron et al (2011)
- Allows an extensive list of robust variance estimators (thanks to the avar package by Kit Baum and Mark Schaffer).
- Works with instrumental-variable and GMM estimators (such as two-step-GMM, LIML, etc.) thanks to the ivreg2 routine by Baum, Schaffer and Stillman.
- Allows multiple heterogeneous slopes (e.g. a separate slope coefficients for each individual).
- Supports all standard Stata features:
- Frequency, probability, and analytic weights.
- Time-series and factor variables.
- Fixed effects and cluster variables can be expressed as factor interactions, for both convenience and speed (e.g. directly using
state#yearinstead of previously usingegen groupto generate the state-year combination). - Postestimation commands such as
predictandtest.
- Allows precomputing results with the
cache()option, so subsequent regressions are faster. - If requested, saves the point estimates of the fixed effects (caveat emptor: these fixed effects may not be consistent nor identifiable; see the Abowd paper for an introduction to the topic).
- Calculates the degrees-of-freedom lost due to the fixed effects (beyond two levels of fixed effects this is still an open problem, but we provide a conservative upper bound).
- Avoids common pitfalls, by excluding singleton groups (see notes), computing correct within- adjusted-R-squares (see initial discussion), etc.
Sergio Correia
Board of Governors of the Federal Reserve
Email: sergio.correia@gmail.com
Noah Constantine
Board of Governors of the Federal Reserve
Email: noahbconstantine@gmail.com
This package wouldn't have existed without the invaluable feedback and contributions of Paulo Guimaraes, Amine Ouazad, Mark E. Schaffer, Kit Baum and Matthieu Gomez. Also invaluable are the great bug-spotting abilities of many users.
Contributors and pull requests are more than welcome. There are a number of extension possibilities, such as estimating standard errors for the fixed effects using bootstrapping, exact computation of degrees-of-freedom for more than two HDFEs, and further improvements in the underlying algorithm.
Note that all the code is written in the current-code folder, which then gets compiled by build.py into the src folder (which combines multiple files in single .ado and .mata files, so they can be installed and copied faster.