Introduction reghdfeimplementstheestimatorfrom Correia,S.(2016).LinearModelswithHigh-DimensionalFixed EffectsAnEfcientandFeasibleEstimator.WorkingPaper. reghdfe uses a different algorithm for high dimensional fixed effects. Multiple R packages aim for the same purpose, including lfe (felm function), fixest (feols function), biglm (biglm function), estimatr (lmrobust function), etc. From my personal experience, estimatr is generally faster than others.. . Explanation When running instrumental-variable regressions with the ivregress package, robust standard errors, and a gmm2s estimator, reghdfe will translate vce (robust) into wmatrix (robust) vce (unadjusted). This maintains compatibility with ivreg2 and other packages, but may unadvisable as described in ivregress (technical note). Nov 15, 2020 Install with ssc install reghdfe. See the linked documentation, as well as "Singletons, Cluster-Robust Standard Errors and Fixed Effects A Bad Mix" and "Linear Models with High-Dimensional Fixed Effects An Efficient and Feasible Estimator".. FixedEffectModelPyHDFE A Python Package for Linear Model with High Dimensional Fixed Effects. FixedEffectModel is a Python Package designed and built by Kuaishou DA ecology group. It provides solutions for linear model with high dimensional fixed effects,including support for calculation in variance (robust variance and multi-way cluster. two-way robust estimator in the setting of dyadic models. The methods and supporting theory for two-way and multi-way clustering and for both OLS and quite general nonlinear estimators are presented in Section 2 and in the Appendix. Like the one-way cluster-robust method, our methods assume that the number of clusters goes to innity.. reghdfe 3.2.9 21feb2016 Sergio Correia (sergio.correiagmail.com) Mata code is first, then main reghdfe .ado, then auxiliary .ado files ----- Mata Code Method of Alternating Projections with Acceleration ----- To debug the mata code, uncomment this three lines, and then -do- the file discard pr drop all clear all Type Aliases local Boolean real scalar local.. Description. reghdfe is a generalization of areg (and xtreg,fe, xtivreg,fe) for multiple levels of fixed effects, and multi-way clustering. For alternative estimators (2sls, gmm2s, liml), as well as additional standard errors (HAC, etc) see ivreghdfe.For nonlinear fixed effects, see. Feb 28, 2021 In particular, the mata reghdfefixpsd() function will only work on the submatrix that excludes cons. This means that the entire matrix might not be positive-semidefinite due to the cons rowcol. However, we shouldn&x27;t care about this, as this should only matter if you are running a test that involves a regressor together with. reghdfe 3.2.9 21feb2016 Sergio Correia (sergio.correiagmail.com) Mata code is first, then main reghdfe .ado, then auxiliary .ado files ----- Mata Code Method of Alternating Projections with Acceleration ----- To debug the mata code, uncomment this three lines, and then -do- the file discard pr drop all clear all Type Aliases local Boolean real scalar local.. As noted above, there are numerous other ways to implement fixed effect models in R. Users may also wish to look at the plm, lme4, and estimatr packages among others. For example, the latters estimatrlmrobust function provides syntax that may be more familar syntax to new R users who are coming over from Stata.. reghdfe reghdfe areg xtreg,fe areg xtreg. Mar 22, 2019 Teams. Q&A for work. Connect and share knowledge within a single location that is structured and easy to search. Learn more. Dec 06, 2018 I find slightly different results when estimating a panel data model in Stata (using the community-contributed command reghdfe) vs. R. Stata cls webuse nlswork, clear xtset idcode year reghdfe .. Nov 09, 2020 &183; residualization by reghdfe and robust and clustered standard errors, with automatic report of estimation results and sample size. Program version This working paper refers to binscatterhist version 2.0, available on SSC. quot;> geography type bigquery. What reghdfe does is the following 1. Before running, you have a dataset in memory that will remain there (so if the dataset is 10gb, that&x27;s 10gb used from the get-go) 2. reghdfe loads the data into Mata, which can potentially double the memory usage if all covariates are double. Nov 15, 2020 reghdfe takes the first approach, though you could implement (3) explicitly by assigning a group.