Getting data from pdfs using the pdftools package
Imputing missing values in parallel using {furrr}
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

In this post, I will explain how you can use the R `gmm`

package to estimate a non-linear model, and more specifically a logit model. For my research, I have to estimate Euler equations using the Generalized Method of Moments. I contacted Pierre Chaussé, the creator of the `gmm`

library for help, since I was having some difficulties. I am very grateful for his help (without him, I'd still probably be trying to estimate my model!).

I will not dwell in the theory too much, because you can find everything you need here. I think it's more interesting to try to understand why someone would use the Generalized Method of Moments instead of maximization of the log-likelihood. Well, in some cases, getting the log-likelihood can be quite complicated, as can be the case for arbitrary, non-linear models (for example if you want to estimate the parameters of a very non-linear utility function). Also, moment conditions can sometimes be readily available, so using GMM instead of MLE is trivial. And finally, GMM is... well, a very general method: every popular estimator can be obtained as a special case of the GMM estimator, which makes it quite useful.

Another question that I think is important to answer is: why this post? Well, because that's exactly the kind of post I would have loved to have found 2 months ago, when I was beginning to work with the GMM. Most posts I found presented the `gmm`

package with very simple and trivial examples, which weren't very helpful. The example presented below is not very complicated per se, but much more closer to a real-world problem than most stuff that is out there. At least, I hope you will find it useful!

For illustration purposes, I'll use data from Marno Verbeek's *A guide to modern Econometrics*, used in the illustration on page 197. You can download the data from the book's companion page here under the section *Data sets* or from the `Ecdat`

package in R. I use the data set from Gretl though, as the dummy variables are numeric (instead of class `factor`

) which makes life easier when writing your own functions. You can get the data set here.

I don't estimate the exact same model, but only use a subset of the variables available in the data set. Keep in mind that this post is just for illustration purposes.

First load the `gmm`

package and load the data set:

```
require("gmm")
data <- read.table("path/to/data/benefits.R", header = T)
attach(data)
```

We can then estimate a logit model with the `glm()`

function:

```
native <- glm(y ~ age + age2 + dkids + dykids + head + male + married + rr + rr2, family = binomial(link = "logit"), na.action = na.pass)
summary(native)
```

```
##
## Call:
## glm(formula = y ~ age + age2 + dkids + dykids + head + male +
## married + rr + rr2, family = binomial(link = "logit"), na.action = na.pass)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.889 -1.379 0.788 0.896 1.237
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.00534 0.56330 -1.78 0.0743 .
## age 0.04909 0.02300 2.13 0.0328 *
## age2 -0.00308 0.00293 -1.05 0.2924
## dkids -0.10922 0.08374 -1.30 0.1921
## dykids 0.20355 0.09490 2.14 0.0320 *
## head -0.21534 0.07941 -2.71 0.0067 **
## male -0.05988 0.08456 -0.71 0.4788
## married 0.23354 0.07656 3.05 0.0023 **
## rr 3.48590 1.81789 1.92 0.0552 .
## rr2 -5.00129 2.27591 -2.20 0.0280 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6086.1 on 4876 degrees of freedom
## Residual deviance: 5983.9 on 4867 degrees of freedom
## AIC: 6004
##
## Number of Fisher Scoring iterations: 4
```

Now comes the interesting part: how can you estimate such a non-linear model with the `gmm()`

function from the `gmm`

package?

For every estimation with the Generalized Method of Moments, you will need valid moment conditions. It turns out that in the case of the logit model, this moment condition is quite simple:

$$ E[X’ * (Y-\Lambda(X’\theta))] = 0 $$

where \( \Lambda() \) is the logistic function. Let's translate this condition into code. First, we need the logistic function:

```
logistic <- function(theta, data) {
return(1/(1 + exp(-data %*% theta)))
}
```

and let's also define a new data frame, to make our life easier with the moment conditions (don't forget to add a column of ones to the matrix, hence the `1`

after `y`

):

```
dat <- data.matrix(cbind(y, 1, age, age2, dkids, dykids, head, male, married,
rr, rr2))
```

and now the moment condition itself:

```
moments <- function(theta, data) {
y <- as.numeric(data[, 1])
x <- data.matrix(data[, 2:11])
m <- x * as.vector((y - logistic(theta, x)))
return(cbind(m))
}
```

The moment condition(s) are given by a function which returns a matrix with as many columns as moment conditions (same number of columns as parameters for just-identified models).

To use the `gmm()`

function to estimate our model, we need to specify some initial values to get the maximization routine going. One neat trick is simply to use the coefficients of a linear regression; I found it to work well in a lot of situations:

```
init <- (lm(y ~ age + age2 + dkids + dykids + head + male + married + rr + rr2))$coefficients
```

And finally, we have everything to use `gmm()`

:

```
my_gmm <- gmm(moments, x = dat, t0 = init, type = "iterative", crit = 1e-25, wmatrix = "optimal", method = "Nelder-Mead", control = list(reltol = 1e-25, maxit = 20000))
summary(my_gmm)
```

```
##
## Call:
## gmm(g = moments, x = dat, t0 = init, type = "iterative", wmatrix = "optimal",
## crit = 1e-25, method = "Nelder-Mead", control = list(reltol = 1e-25,
## maxit = 20000))
##
##
## Method: iterative
##
## Kernel: Quadratic Spectral
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.9090571 0.5751429 -1.5805761 0.1139750
## age 0.0394254 0.0231964 1.6996369 0.0891992
## age2 -0.0018805 0.0029500 -0.6374640 0.5238227
## dkids -0.0994031 0.0842057 -1.1804799 0.2378094
## dykids 0.1923245 0.0950495 2.0234150 0.0430304
## head -0.2067669 0.0801624 -2.5793498 0.0098987
## male -0.0617586 0.0846334 -0.7297189 0.4655620
## married 0.2358055 0.0764071 3.0861736 0.0020275
## rr 3.7895781 1.8332559 2.0671300 0.0387219
## rr2 -5.2849002 2.2976075 -2.3001753 0.0214383
##
## J-Test: degrees of freedom is 0
## J-test P-value
## Test E(g)=0: 0.00099718345776501 *******
##
## #############
## Information related to the numerical optimization
## Convergence code = 10
## Function eval. = 17767
## Gradian eval. = NA
```

Please, notice the options `crit=1e-25,method="Nelder-Mead",control=list(reltol=1e-25,maxit=20000)`

: these options mean that the Nelder-Mead algorithm is used, and to specify further options to the Nelder-Mead algorithm, the `control`

option is used. This is very important, as Pierre Chaussé explained to me: non-linear optimization is an art, and most of the time the default options won't cut it and will give you false results. To add insult to injury, the Generalized Method of Moments itself is very capricious and you will also have to play around with different initial values to get good results. As you can see, the Convergence code equals 10, which is a code specific to the Nelder-Mead method which indicates «degeneracy of the Nelder–Mead simplex.» . I'm not sure if this is a bad thing though, but other methods can give you better results. I'd suggest you try always different maximization routines with different starting values to see if your estimations are robust. Here, the results are very similar to what we obtained with the built-in function `glm()`

so we can stop here.

Should you notice any error whatsoever, do not hesitate to tell me.