Generalized Linear Model (IRLS)

GLM via IRLS by group with weights, clustering, and HDFE


Run gtools, upgrade to update gtools to the latest stable version.


gglm is in beta and meant for testing; use in production NOT recommended. (To enable beta features, define global GTOOLS_BETA = 1.)

gglm computes fast GLM regression coefficients and standard errors by group. Its basic functionality is similar to that of the user-written rangestat (reg) or regressby, except that it computes GLM via IRLS instead of OLS; in addition, gglm allows weights, clustering, and HDFE by group. This program is not intended as a substitute for glm or similar commands. Support for some estimation operations are planned; however, gglm does not compute any significance tests and no post-estimation commands are available.


gglm depvar indepvars [if] [in] [weight] [, ///
    by() absorb() family() options]

Support for different link functions may be added in future releases. At the moment only the cannonical link for each family() is available:

Family Link Default Output
binomial logit GtoolsLogit
poisson log GtoolsPoisson

By default, results are saved into a mata class object named after the model estimated, as noted above. For details, the desc() method is available, e.g. mata GtoolsLogit.desc(). The name and contents can be modified via the mata() option. The results can also be saved into variables via gen() or prefix() (either can be combined with mata(), but not each other).

Note that extended varlist syntax is not supported. Further, fweights behave differently than other weighting schemes; that is, this assumes that the weight referes to the number of available observations.


Save Results

  • mata(name, [nob nose]) Specify name of output mata object and whether to save b and se

  • gen(...) Specify any of b(varlist), se(varlist), and hdfe(varlist). One per covariate is required (hdfe() also requires one for the dependent variable).

  • prefix(...) Specify any of b(str), se(str), and hdfe(str). A single prefix is allowed.

  • replace Allow replacing existing variables.


  • family(str) Model to compute. Support for different links is planned for a future release. Currently available families (and corresponding link functions): binomial (logit), poisson (log).
  • by(varlist) Group statistics by variable.
  • robust Robust SE.
  • cluster(varlist) One-way or nested cluster SE.
  • absorb(varlist) Multi-way high-dimensional fixed effects.
  • hdfetol(real) Tolerance level for HDFE algoritm (default 1e-8).
  • algorithm(str) Algorithm used to absorb HDFE: CG (conjugate gradient; default) MAP (alternating projections), SQUAREM (squared extrapolation), IT (Irons and Tuck).
  • maxiter(int) Maximum number of algorithm iterations (default 100,000). Pass . for unlimited iterations.
  • tolerance(real) Convergence tolerance (default 1e-8). Note the convergence criterion is |X(k + 1) - X(k)| < tol for the kth iteration, with || the sup norm (i.e. largest element). This is a tighter criteria than the squared norm and setting the tolerance too low might negatively impact performance or with some algorithms run into numerical precision problems.
  • traceiter Trace algorithm iterations.
  • standardize Standardize variables before algorithm (may be faster but is slighty less precise).
  • noconstant Whether to add a constant (cannot be combined with absorb()).
  • glmtol(real) Tolerance level for IRLS algoritm (default 1e-8).
  • glmiter(int) Maximum number of iterations for IRLS (default 1000).

Gtools options

(Note: These are common to every gtools command.)

  • compress Try to compress strL to str#. The Stata Plugin Interface has only limited support for strL variables. In Stata 13 and earlier (version 2.0) there is no support, and in Stata 14 and later (version 3.0) there is read-only support. The user can try to compress strL variables using this option.

  • forcestrl Skip binary variable check and force gtools to read strL variables (14 and above only). Gtools gives incorrect results when there is binary data in strL variables. This option was included because on some windows systems Stata detects binary data even when there is none. Only use this option if you are sure you do not have binary data in your strL variables.

  • verbose prints some useful debugging info to the console.

  • benchmark or bench(level) prints how long in seconds various parts of the program take to execute. Level 1 is the same as benchmark. Levels 2 and 3 additionally prints benchmarks for internal plugin steps.

  • hashmethod(str) Hash method to use. default automagically chooses the algorithm. biject tries to biject the inputs into the natural numbers. spooky hashes the data and then uses the hash.

  • oncollision(str) How to handle collisions. A collision should never happen but just in case it does gtools will try to use native commands. The user can specify it throw an error instead by passing oncollision(error).


gglm estimates GLM via IRLS, optionally weighted, by group, with cluster SE, and/or with multi-way high-dimensional fixed effects. The results are by default saved into a mata object (e.g. GtoolsLogit, GtoolsPoisson, and so on; run mata GtoolsLogit.desc() for details). The following data is stored:

regression info

    string scalar caller
        model used; "glogit", "gpoisson", etc.

    real scalar kx
        number of (non-absorbed) covariates

    real scalar cons
        whether a constant was added automagically

    real scalar saveb
        whether b was stored

    real matrix b
        J by kx matrix with regression coefficients

    real scalar savese
        whether se was stored

    real matrix se
        J by kx matrix with corresponding standard errors

    string scalar setype
        type of SE computed (homoskedastic, robust, or cluster)

    real scalar absorb
        whether any FE were absorbed

    string colvector absorbvars
        variables absorbed as fixed effects

    string colvector clustervars
        cluster variables

    real scalar by
        whether there were any grouping variables

    string rowvector byvars
        grouping variable names

    real scalar J
        number of levels defined by grouping variables

    class GtoolsByLevels ByLevels
        grouping variable levels; see e.g. GtoolsLogit.ByLevels.desc() for details

variable levels (empty if without -by()-)

    real scalar ByLevels.anyvars
        1: any by variables; 0: no by variables

    real scalar ByLevels.anychar
        1: any string by variables; 0: all numeric by variables

    string rowvector ByLevels.byvars
        by variable names

    real scalar ByLevels.kby
        number of by variables

    real scalar ByLevels.rowbytes
        number of bytes in one row of the internal by variable matrix

    real scalar ByLevels.J
        number of levels

    real matrix ByLevels.numx
        numeric by variables

    string matrix ByLevels.charx
        string by variables

    real scalar ByLevels.knum
        number of numeric by variables

    real scalar ByLevels.kchar
        number of string by variables

    real rowvector ByLevels.lens
        > 0: length of string by variables; <= 0: internal code for numeric variables

    real rowvector
        map from index to numx and charx

Methods and Formulas


We aim to model the conditional expectation of some outcome $y_i$ given a set of covariates $x_i$. GLM allows us to estimate a class of models of the form $$ g(E[y_i | x_i]) = x_i \beta $$

where $g(\cdot)$ is a so-called link function that allows us to model the (linked) conditional expectation as linear. Now recall the exponential family of distributions, where $$ f(y; \theta, \varphi) = \exp \left[ \dfrac{y \theta - b(\theta)}{a(\varphi)} + c(y, \varphi) \right] $$

and suppose $y_i | x_i \sim f(y_i; x_i^\prime \beta, \varphi)$, so that $$ \begin{align} E[y_i | x_i] & = \mu_i = b^\prime(x_i^\prime \beta) = g^{-1}(x_i^\prime \beta) \end{align} $$

We can estimate this model via MLE, where we maximize the log-likelihood $$ \begin{align} \log L & = \sum_i \log f(y_i; x_i^\prime \beta, \varphi) = \sum_i \left[ \dfrac{y_i \cdot (x_i^\prime \beta) - b(x_i^\prime \beta)}{a(\varphi)} + c(y_i, \varphi) \right] \end{align} $$

with $y_i$ the dependent variable, $x_i$ covariates, and $\beta$ the vector of parameters to be estimated. The MLE estimator $\widehat{\beta}$ is then given by the solving the FOC with respect to $\beta$ $$ \begin{align} 0 & = \sum_i \dfrac{y_i - b^{\prime}(x_i^\prime \beta)}{a(\varphi)} x_i = \sum_i \dfrac{y_i - \mu_i}{a(\varphi)} x_i \end{align} $$

with $x_i$ the vector of covariates. One way to solve the above equaiton is to apply Newton's method (Newton-Raphson) as shown by Nelder and Wedderburn (1972). To find the zeros of a vector-valued function $g(t)$, given an initial guess $t_0$, we can iterate $$ \begin{align} t_{n + 1} & = t_n - [J_g(t_n)]^{-1} g(t_n) \end{align} $$

with $J_g(\cdot)$ the Jacobian matrix with the derivatives of each of the elements of $g$ with respect to each of its arguments. Let $g(\beta)$ denote the gradient of the log-likelihood and $H(\beta)$ the Hessian, so that $H(\beta)$ is the Jacobian matrix of $g(\beta)$. That is, $$ \begin{align} g(\beta) & = \sum_i \dfrac{y_i - b^{\prime}(x_i^\prime \beta)}{a(\varphi)} x_i = a(\varphi)^{-1} X^\prime \left(Y - \mu\right) \\ H(\beta) & = - \sum_i \dfrac{b^{\prime\prime}(x_i^\prime \beta)}{a(\varphi)} x_i x_i^\prime = - a(\varphi)^{-1} X^\prime W X \end{align} $$

where $W$ is a diagonal matrix with $w_{ii} = b^{\prime\prime}(x_i^\prime \beta)$ and $\mu$ is a vector of stacked $\mu_i = b^\prime(x_i^\prime \beta)$. Now given an initial guess $\widehat{\beta}^{(0)}$, noting the $a(\varphi)$ cancel, $$ \begin{align} \widehat{\beta}^{(r + 1)} & = \widehat{\beta}^{(r)} - H\big(\widehat{\beta}^{(r)}\big)^{-1} g\big(\widehat{\beta}^{(r)}\big) \\ & = \widehat{\beta}^{(r)} + \big(X^\prime W^{(r)} X\big)^{-1} X^\prime \left(Y - \mu^{(r)}\right) \\ & = \big(X^\prime W^{(r)} X\big)^{-1} \left( \big(X^\prime W^{(r)} X\big) \widehat{\beta}^{(r)} + X^\prime \left(Y - \mu^{(r)}\right) \right) \\ & = \big(X^\prime W^{(r)} X\big)^{-1} X^\prime W^{(r)} \left( X \widehat{\beta}^{(r)} + \big(W^{(r)}\big)^{-1} \left(Y - \mu^{(r)}\right) \right) \\ & = (X^\prime W^{(r)} X)^{-1} X^\prime W^{(r)} z^{(r)} \\ z^{(r)} & \equiv \eta^{(r)} + \big(W^{(r)}\big)^{-1} \left(Y - \mu^{(r)}\right) \\ \eta^{(r)} & \equiv X \widehat{\beta}^{(r)} \end{align} $$

That is, $\widehat{\beta}^{(r + 1)}$ is the result of WLS with $z^{(r)}$ as the left-hand variable, $X$ as covariates, and $W^{(r)}$ as the weighting matrix. Note that we can start from an initial guess $\eta^{(0)}$, $\mu^{(0)}$, or $\beta^{(0)}$; however, at any subsequent iteration all the variables are updated based on $\beta^{(r)}$.

We iterate until convergence. At each step, we compute the deviance $\delta^{(r + 1)}$ (see below for details). We stop if the largest relative absolute difference between $\delta^{(r)}$ and $\delta^{(r + 1)}$, denoted $\Delta^{(r + 1)}$, is within glmtol() $$ \Delta^{(r + 1)} \equiv \max_i \frac{ |\delta^{(r + 1)}_i - \delta^{(r)}_i| }{ |\delta^{(r)}_i + 1| } $$

$\delta^{(0)}$ is set to $1$ and the default tolerance is $1\mathrm{e}{-8}$. If the tolerance criteria is met then each variable is set to their value after the $r$th iteration (i.e. $\widehat{\beta}$ to $\widehat{\beta}^{(r + 1)}$, $W$ to $W^{(r + 1)}$, and so on). If convergence is not achieved, however, and the maximum number of iterations is reached instead (see glmiter()) then the program exits with error.

$$ \delta^{(r + 1)} = 2 \cdot (\log(Y / \mu^{(r + 1)}) - (Y - \mu^{(r + 1)})) $$

(if $Y_i = 0$ then $\delta^{(r + 1)}_i$ is also set to $0$).

The following table summarizes for various families:

Family Link $a(\varphi)$ $b(\theta)$ $c(y, \varphi)$
binomial logit $1 / \varphi$ $\log(1 + e^\theta)$ $\log(C(\varphi, y \varphi))$
poisson log $1$ $e^\theta$ $- \log(y!)$

For poisson, $b^{\prime}(\theta) = e^\theta, b^{\prime\prime}(\theta) = e^\theta$ and $$ \begin{align} \eta^{(r)} & = X \beta^{(r)} \\ \mu^{(r)} & = \exp(\eta^{(r)}) \\ W^{(r)} & = \text{diag}\{\mu^{(r)}_1, \ldots, \mu^{(r)}_n\} \\ z^{(r)} & = \eta^{(r)} + (Y - \mu^{(r)}) / \mu^{(r)} \end{align} $$

In our specific implementation, we follow Guimarães (2014) and implement an initial guess $\mu^{(0)} = (Y + \overline{Y}) / 2$ then define $\eta^{(0)}, z^{(0)}$, and $W^{(0)}$. In all subsequent iterations, however, the variables are defined as in the equations above using $\beta^{(r)}$. Note a column of ones is automatically appended to $X$ unless the option noconstant is passed or absorb(varlist) is requested.

Collinearity and Inverse

$X^\prime W^{(r)} X$ is is scaled by the inverse of $M = \max_{ij} X^\prime W^{(r)} X$ and subsequently decomposed into $L D L^\prime$, with $L$ lower triangular and $D$ diagonal (note $X^\prime X$ is a symmetric positive semi-definite matrix). If $D_{ii}$ is numerically zero then the $i$th column is flagged as collinear and subsequently excluded from all computations (specifically if $D_{ii} < k \cdot 2.22\mathrm{e}{-16}$, where $k$ is the number of columns in $X$ and $2.22\mathrm{e}{-16}$ is the machine epsilon in 64-bit systems).

The inverse is then computed as $(L^{-1})^\prime D^{-1} L^{-1} M^{-1}$, excluding the columns flagged as collinear. If the determinant of $X^\prime W^{(r)} X$ is numerically zero ($< 2.22\mathrm{e}{-16}$) despite excluding collinear columns, a singularity warning is printed. The coefficients for collinear columns are coded as $0$ and their standard errors are coded as missing (.).

Standard Errors

The standard error of the $i$th coefficient is given by $$ SE_i = \sqrt{\frac{n}{n - 1} \widehat{V}_{ii}} $$

where $\frac{n}{n - 1}$ is a small-sample adjustment and $n \widehat{V}$ is a consistent estimator of the asymptotic variance of $\widehat{\beta}$. Note we compute the small-sample adjustment to match the standard errors returned by Stata's poisson and logit programs. The standard error of collinear columns is coded as missing (.).

By default, homoskedasticity-consistent standard errors are computed: $$ \begin{align} \widehat{V} & = (X^\prime W X)^{-1} \widehat{\sigma} \\ \widehat{\sigma} & = \widehat{\varepsilon}^\prime \widehat{\varepsilon} / n \end{align} $$

where $$ \widehat{\varepsilon} = z - X \widehat{\beta} $$

is the error of the WLS fit for the $r$th iteration. If robust is passed then White heteroskedascitity-consistent standard errors are computed instead: $$ \begin{align} \widehat{\Sigma} & = \text{diag}\{\widehat{\varepsilon}_1^2, \ldots, \widehat{\varepsilon}_n^2\} \\ \widehat{V} & = (X^\prime W X)^{-1} X^\prime W \widehat{\Sigma} W X (X^\prime W X)^{-1} \end{align} $$


If cluster(varlist) is passed then nested cluster standard errors are computed (i.e. the rows of varlist define the groups). Let $j$ denote the $j$th group defined by varlist and $J$ the number of groups. Then $$ \begin{align} \widehat{V} & = (X^\prime W X)^{-1} \left( \sum_{j = 1}^J \widehat{u}_j \widehat{u}_j^\prime \right) (X^\prime W X)^{-1} \\ \widehat{u}_j & = X_j^\prime W_j \widehat{\varepsilon}_j \end{align} $$

with $X_j^\prime$ the matrix of covariates with observations from the $j$th group, $\widehat{\varepsilon}_j$ the vector with errors from the $j$th group, and $W_j$ the diagonal matrix with entries corresponding to the weights for the $j$th group (i.e. $\mu_j$). (Note another way to write the sum in $\widehat{V}$ is as $U^\prime U$, with $U^\prime = [u_1 ~~ \cdots ~~ u_J]$.) Finally, the standard error is given by

$$ SE_i = \sqrt{\frac{J}{J - 1} \widehat{V}_{ii}} $$

Note we compute the small-sample adjustment to match the standard errors returned by Stata's poisson and logit programs.


Let $w$ denote the weighting variable and $w_i$ the weight assigned to the $i$th observation. $\widehat{\beta}$ is obtained in the same way except that at each iteration step, we use $$ \widetilde{W}^{(r)} = \text{diag}\{\mu^{(r)}_1 w_1, \ldots, \mu^{(r)}_n w_n\} $$

as the weighting matrix instead of $W^{(r)}$. fweights runs the regression as if there had been $w_i$ copies of the $i$th observation. As such, $n_w = \sum_{i = 1}^n w_i$ is used instead of $n$ to compute the small-sample adjustment, and $$ \begin{align} \widehat{\Sigma} & = \text{diag}\{\widehat{\varepsilon}_1^2 w_1, \ldots, \widehat{\varepsilon}_n^2 w_n\} \\ \widehat{V} & = (X^\prime \widetilde{W} X)^{-1} X^\prime W \widetilde{\Sigma} W X (X^\prime \widetilde{W} X)^{-1} \end{align} $$

is used for robust standard errors. There are a few ways to write this, but the idea is that this is not the variance of the WLS estimate, but the variance if there had been $w_i$ copies of the $i$th observation. The IRLS algorithm computes WLS already, so the $i$th weight (after convergence) is $\mu_i w_i$. However, we want $w_i$ copies of the $i$th observation instead. Hence the correct weight is $W$ but we multiply $\widehat{\varepsilon}_i^2$ with $w_i$ to mimic the scenario when we have $w_i$ copies of the $i$th row.

In contrast, for other weights (aweights being the default), $n$ is used to compute the small-sample adjustment, and $n \widehat{V}$ estimates the asymptotic variance of the WLS estimator. That is, $$ \begin{align} \widehat{V} & = (X^\prime \widetilde{W} X)^{-1} X^\prime \widetilde{W} \widehat{\Sigma} \widetilde{W} X (X^\prime \widetilde{W} X)^{-1} \end{align} $$

In other words, we can replace $W$ with $\widetilde{W}$ entirely. With clustering, these two methods of computing $\widehat{V}$ will actually coincide, and the only difference between fweights and other weights will be the way the small-sample adjustment is computed.

Finally, with weights and HDFE, the iterative de-meaning (see below) uses the weighted mean.


Multi-way high-dimensional fixed effects can be added to any regression via absorb(varlist). That is, coefficients at each iteration are computed as if the levels of each variable in varlist had been added to the regression as fixed effects. It is well-known that with one fixed effect $\widehat{\beta}^{(r)}$ can be estimated via the within transformation (i.e. de-meaning the dependent variable and each covariate by the levels of the fixed effect; this can also be motivated via the Frisch-Waugh-Lovell theorem). That is, with one fixed effect we have the following algorithm at each iteration:

  1. Compute $\overline{z}^{(r)}$ and $\overline{X}$ with the weighted mean of $z^{(r)}$ and $X$ by the levels of the fixed effect. The weighting vector is $\mu^{(r)}$.

  2. Replace $z^{(r)}$ and $X$ with $z^{(r)} - \overline{z}^{(r)}$ and $X - \overline{X}$, respectively.

  3. Compute WLS normally with $z^{(r)}$ and $X$ de-meaned, making sure to include the number of fixed effects in the small-sample adjustment of the standard errors.

With multiple fixed effects, the same can be achieved by continuously de-meaning by the levels of each of the fixed effects. Following Correia (2017a, p. 12), we have instead:

  1. Let $\alpha_m$ denote the $m$th fixed effect, $M$ the number of fixed effects (i.e. the number of variables to include as fixed effects), and $m = 1$.

  2. Compute $\overline{z}^{(r)}$ and $\overline{X}$ with the weighted mean of $z^{(r)}$ and $X$ by the levels of $\alpha_m$. The weighting vector is $\mu^{(r)}$.

  3. Replace $z^{(r)}$ and $X$ with $z^{(r)} - \overline{z}^{(r)}$ and $X - \overline{X}$, respectively.

  4. Repeat steps 2 and 3 for $m = 1$ through $M$.

  5. Repeat steps 1 through 4 until convergence, that is, until neither $Y$ nor $X$ change across iterations.

  6. Compute WLS normally with the iteratively de-meaned $z^{(r)}$ and $X$, making sure to include the number of fixed effects across all fixed effect variables in the small-sample adjustment of the standard errors.

This is known as the Method of Alternating Projections (MAP). Let $A_m$ be a matrix with dummy variables corresponding to each of the levels of $\alpha_m$, the $m$th fixed effect. MAP is so named because at each step, $z^{(r)}$ and $X$ are projected into the null space of $A_m$ for $m = 1$ through $M$. (In particular, with $Q_m = I - A_m (A_m^\prime A_m)^{-1} A_m^\prime$ the orthogonal projection matrix, steps 2 and 3 replace $z^{(r)}$ and $X$ with $Q_m z^{(r)}$ and $Q_m X$, respectively.)

Correia (2017a) actually proposes several ways of accelerating the above algorithm; we have yet to explore any of his proposed modifications (see Correia's own reghdfe and ppmlhdfe packages for an implementation of the methods discussed in his paper).

Finally, we note that in step 5 we detect "convergence" as the maximum element-wise absolute difference between $z^{(r)}, X$ and $Q_m z^{(r)}, Q_m X$, respectively (i.e. the $l_{\infty}$ norm). This is a tighter tolerance criterion than the one in Correia (2017a, p. 12), which uses the $l_2$ norm, but by default we also use a tolerance of $1\mathrm{e}{-8}$. The trade-off is precision vs speed. The tolerance criterion is hard-coded but the level can be modified via hdfetol(). A smaller tolerance will converge faster but the point estimates will be less precise (and the collinearity detection algorithm will be more susceptible to failure).

Technical Notes

Ideally I would have been keen to use a standard linear algebra library available for C. However, I was unable to find one that I could include as part of the plugin without running into cross-platform compatibility or installation issues (specifically I was unable to compile them on Windows or OSX; I do not have access to physical hardware running either OS, so adding external libraries is challenging). Hence I had to code all the linear algebra commands that I wished to use.

As far as I can tell, this is only noticeable when it comes to matrix multiplication. I use a naive algorithm with no optimizations. This is the main bottleneck in regression models with multiple covariates (and the main reason regress is faster without groups or clustering). Suggestions on how to improve this algorithm are welcome.

Missing Features

This software will remain in beta at least until the following are added:

In addition, some important features are missing:

  • Option to estimate the fixed effects (i.e. the coefficients of each HDFE group) included in the regression.

  • Option to estimate standard errors under multi-way clustering.

  • Faster HDFE algorithm. At the moment the method of alternating projections (MAP) is used, which has very poor worst-case performance. While gregress is fast in our benchmarks, it does not have any safeguards against potential corner cases. (See Correia (2017a) for notes on this issue.)

  • Support for Stata's extended varlist syntax.


Note gregress is in beta. To enable enable beta features, define global GTOOLS_BETA = 1.

You can download the raw code for the examples below here


webuse lbw, clear
gglm low age lwt smoke ptl ht ui, absorb(race) family(binomial)
mata GtoolsLogit.print()

gen w = _n
gglm low age lwt smoke ptl ht ui [fw = w], absorb(race) family(binomial)
mata GtoolsLogit.print()

webuse ships, clear
expand 2
gen by = 1.5 - (_n < _N / 2)
gen w = _n
gen _co_75_79  = co_75_79
qui tab ship, gen(_s)

gglm accident op_75_79 co_65_69 co_70_74 co_75_79 [fw = w], robust family(poisson)
mata GtoolsPoisson.print()

gglm accident op_75_79 co_65_69 co_70_74 co_75_79 _co_75_79 [pw = w], cluster(ship) family(poisson)
mata GtoolsPoisson.print()

gglm accident op_75_79 co_65_69 co_70_74 co_75_79 _s*, absorb(ship) cluster(ship) family(poisson)
mata GtoolsPoisson.print()

gglm accident op_75_79 co_65_69 co_70_74 co_75_79, by(by) absorb(ship) robust family(poisson)
mata GtoolsPoisson.print()

Basic benchmark

local N 1000000
local G 10000
set obs `N'
gen g1 = int(runiform() * `G')
gen g2 = int(runiform() * `G')
gen g3 = int(runiform() * `G')
gen g4 = int(runiform() * `G')
gen x3 = runiform()
gen x4 = runiform()
gen x1 = x3 + runiform()
gen x2 = x4 + runiform()
gen l  = int(0.25 * x1 - 0.75 * x2 + g1 + g2 + g3 + g4 + 20 * rnormal())

timer clear
timer on 1
gglm l x1 x2, absorb(g1 g2 g3) mata(greg) family(poisson)
timer off 1
mata greg.print()
timer on 2
ppmlhdfe l x1 x2, absorb(g1 g2 g3)
timer off 2

timer on 3
gglm l x1 x2, absorb(g1 g2 g3) cluster(g4) mata(greg) family(poisson)
timer off 3
mata greg.print()
timer on 4
ppmlhdfe l x1 x2, absorb(g1 g2 g3) vce(cluster g4)
timer off 4

timer list

   1:      3.22 /        1 =       3.2160
   2:     29.64 /        1 =      29.6380
   3:      3.31 /        1 =       3.3140
   4:     31.32 /        1 =      31.3190


The idea for the FE absorption used here is from Correia (2017a). The conjugate gradient algorithm is from Hernández-Ramos, Escalante, and Raydan (2011) and implemented following Correia (2017b). The SQUAREM algorithm is from Varadhan and Roland (2008) and Varadhan (2016). Irons and Tuck (1969) method implemented following Ramière and Helfer (2015).

  • Correia, Sergio (2015). "Singletons, Cluster-Robust Standard Errors and Fixed Effects: A Bad Mix" Working Paper. Accessed January 16th, 2020. Available at

  • Correia, Sergio, Paulo Guimarães, and Thomas Zylkin (2019). "Verifying the existence of maximum likelihood estimates for generalized linear models." arXiv:1903.01633v5 [econ.EM]. Accessed January 16th, 2020. Available at

  • Guimarães, Paulo (2014). "POI2HDFE: Stata module to estimate a Poisson regression with two high-dimensional fixed effects." Statistical Software Components S457777, Boston College Department of Economics, revised 16 Sep 2016. Accessed January 16th, 2020. Available at

  • Nelder, John A. and Wedderburn, Robert W (1972). "Generalized Linear Models." Journal of the Royal Statistical Society. Series A (General), 135 (3): 370-384. Accessed September 12th, 2020.

  • Correia, Sergio (2017a). "Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator" Working Paper. Accessed January 16th, 2020. Available at

  • Correia Sergio (2017b). "reghdfe: Stata module for linear and instrumental-variable/GMM regression absorbing multiple levels of fixed effects." Statistical Software Components S457874, Boston College Department of Economics. Accessed March 6th, 2022. Available at

  • Hernández-Ramos, Luis M., René Escalante, and Marcos Raydan. 2011. "Unconstrained Optimization Techniques for the Acceleration of Alternating Projection Methods." Numerical Functional Analysis and Optimization, 32(10): 1041–66.

  • Varadhan, Ravi and Roland, Christophe. 2008. "Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm."" Scandinavian Journal of Statistics, 35(2): 335–353.

  • Varadhan, Ravi (2016). "SQUAREM: Squared Extrapolation Methods for Accelerating EM-Like Monotone Algorithms." R package version 2016.8-2.

  • Irons, B. M., Tuck, R. C. (1969). "A version of the Aitken accelerator for computer iteration." International Journal for Numerical Methods in Engineering 1(3): 275–277.

  • Ramière, I., Helfer, T. (2015). "Iterative residual-based vector methods to accelerate fixed point iterations." Computers & Mathematics with Applications 70(9): 2210–2226