In this note we identify and illustrate some shortcomings of the poisson com- mand in STATA. Specifically, we point out that the command fails to check for the existence of the estimates and show that it is very sensitive to numerical problems. While these are serious problems that may prevent users from obtaining estimates, or even produce spurious and misleading results, we show that the informed user often has available simple work-arounds for these problems.

J.C.M. Santos Silva

Silvana Tenreyro

English

Research Papers in Economics

ISSN 1755-5361 
   
        
         
 
  Discussion Paper Series 
 
     
    
     
 
Poisson: Some convergence issues 
 
J.M.C. Santos Silva & Silvana Tenreyro 
 
 
Note : The Discussion Papers in this series are prepared by members of the Department of 
Economics, University of Essex, for private circulation to interested readers. They often 
represent preliminary reports on work in progress and should therefore be neither quoted nor 
referred to in published work without the written consent of the author. 
                                   University of Essex 
 
 
 
        D e p a r t m e n t   o f   E c o n o m i c s  
 
 
 
No. 695 January 2011 poisson: Some convergence issues￿
J.M.C. Santos Silvay Silvana Tenreyroz
January 6, 2011
Abstract
In this note we identify and illustrate some shortcomings of the poisson com-
mand in STATA. Speci￿cally, we point out that the command fails to check for the
existence of the estimates and show that it is very sensitive to numerical problems.
While these are serious problems that may prevent users from obtaining estimates,
or even produce spurious and misleading results, we show that the informed user
often has available simple work-arounds for these problems.
Key words: Collinearity, Complete separation, Numerical problems, Perfect prediction.
￿We are very grateful to an anonymous referee for most valuable comments and suggestions. We
also thank Styliani Christodoulopoulou for helpful discussions and comments on an earlier version of the
paper. The usual disclaimer applies. Santos Silva gratefully acknowledges partial ￿nancial support from
Funda￿ªo para a CiŒncia e Tecnologia (FEDER/POCI 2010).
yUniversity of Essex and CEMAPRE. Wivenhoe Park, Colchester CO4 3SQ, United Kingdom. Fax:
+44 (0)1206 872724. E-mail: jmcss@essex.ac.uk.
zLondon School of Economics, CREI, CEP, and CEPR. Department of Economics, s.600. St: Clement￿ s
Building. Houghton St., London WC2A 2AE, United Kingdom. Fax: +44 (0)20 78311840. E-mail:
s.tenreyro@lse.ac.uk.
11. INTRODUCTION
Besides being the most widely used estimator for count data (see Winkelmann, 2008,
and Cameron and Trivedi, 1997), Poisson regression is also becoming increasingly used to
estimate multiplicative models for other non-negative data (see, among others, Manning
and Mullahy, 2001, and Santos Silva and Tenreyro, 2006). The availability in STATA
of a command that estimates Poisson regression has been an important reason for the
increasing popularity of this estimator. However, researchers using Poisson regression,
especially those using it to estimate gravity equations as recommended by Santos Silva
and Tenreyro (2006), often ￿nd that the algorithm implemented in STATA￿ s poisson
command does not converge. There are two main reasons for this. First, as noted by Santos
Silva and Tenreyro (2010), there are instances in which the estimates do not exist and if
that is the case the convergence of the algorithm used to maximize the likelihood function
can only be spurious. Second, even when the estimates exist, researchers using STATA
may have trouble to get Poisson regression estimates because the poisson command is
very sensitive to numerical problems. In this note we describe how researchers can identify
some of the situations that may lead to convergence problems and propose some simple
work-arounds.
2. THE NON EXISTENCE OF THE ESTIMATES
Let yi and xi denote respectively the variate of interest and the vector of covariates,
and assume that the researcher speci￿es E(yijxi) = exp(x0
i￿). In a sample of size n, ^ ￿, the
Poisson regression estimate of ￿, is de￿ned by
n X
i=1
h
yi ￿ exp
￿
x
0
i^ ￿
￿i
xi = 0. (1)
The form of (1) makes clear that ￿ will be consistently estimated as long as the condi-
tional mean is correctly speci￿ed. That is, the only condition required for the consistency
of the estimator is that E(yijxi) = exp(x0
i￿). This is the well known pseudo-maximum
likelihood result of Gourieroux, Monfort and Trognon (1984).
2However, Santos Silva and Tenreyro (2010) have shown that ^ ￿ does not always exist
and that its existence depends on the data con￿guration. In particular, the estimates may
not exist if there is perfect collinearity for the sub-sample with positive observations of
yi.1 If the estimates do not exist, it is either impossible for the estimation algorithm to
converge or convergence is spurious. The following STATA code illustrates the situation
where convergence is not achieved:2
drawnorm x1, n(1000) seed(101010) double clear
generate double u=rpoisson(1)
generate y=exp(1+10*x1)*u
generate double x2=(y==0)
poisson y x1 x2, robust
An example where the convergence is spurious is given by the code below:
drawnorm x1, n(1000) seed(101010) double clear
generate double y=rpoisson(1)
generate double x2=(y==0)
poisson y x1 x2, robust
The non-existence of the maximum likelihood estimates in Poisson regression is anal-
ogous to what happens in binary choice models when there is complete separation or
quasi-complete separation, as described by Albert and Anderson (1984) and Santner and
Du⁄y (1986). In the case of binary models, it is standard to check for the existence of
the estimates before starting the actual estimation. In contradistinction, the poisson
command in STATA does not check for the existence of the estimates and therefore it is
important that users investigate whether or not the estimates exist. Because the regres-
sors that may cause the non-existence of the estimates are characterized by their perfect
collinearity with the others for the sub-sample with yi > 0, they can easily be identi￿ed in
STATA by using a simpli￿ed version of the three-step method suggested by Santos Silva
and Tenreyro (2010):
1See also Haberman (1973).
2The code used in this note produces the desired results in STATA/IC 11.1 for Windows (32-bit).
Using other ￿ avours of STATA, for example MP versions, may lead to di⁄erent outcomes.
3Step 1: Construct a subset of explanatory variable, say ~ xi, comprising only the regressors
that are not collinear for the observations with yi > 0;
Step 2: Using the full sample, run the Poisson regression of yi on ~ xi.
The following code, where it is assumed that all variables with names starting with x
are regressors, illustrates the implementation of the procedure:3
local _rhs "x*"
_rmcoll ￿_rhs￿ if y>0
poisson y ￿r(varlist)￿, robust
This procedure ensures that the estimates exist by eliminating all potentially problem-
atic regressors, even those that actually do not lead to the non-existence of the maximum
likelihood estimates.4 Therefore, the researcher should subsequently investigate one-by-
one all the variables that were dropped to see if any of them can be included in the model.
Carefully investigation of the variables to be excluded is particularly important when the
model contains sets of dummies with several categories: in this case dropping one of the
dummies implies an arbitrary rede￿nition of the reference category which is unlikely to be
sensible. In any case, dropping some regressors should never be an automatic procedure
because it changes the model speci￿cation and therefore the researcher should carefully
consider what is the best way to ￿nd an interesting speci￿cation for which the (pseudo)
maximum likelihood estimates exist.
It is worth noting that the non existence of the estimates can also occur in any regression
model where the conditional mean is speci￿ed in such a way that its image does not include
all the points in the support of the dependent variable. Therefore, unless the data are
strictly positive, this problem can occur not only in the Poisson regression but also in other
models specifying E(yijxi) = exp(x0
i￿), and in models for limited dependent variables like
the Tobit (Tobin, 1958). In all these cases, the identi￿cation of the problematic regressors
can be done using methods akin to the one described above.
3We are grateful to Markus Baldauf for help with the development of an earlier version of this code
and to an anonymous referee for suggesting this much simple version using the _rmcoll command.
4A less strict criterion to select the regressors to be dropped is used by default in the ppml command
brie￿ y discussed below.
43. NUMERICAL DIFFICULTIES
Even if the (pseudo) maximum likelihood estimates of the Poisson regression exist,
STATA may have di¢ culty identifying them due to the sensitivity to numerical problems
of the algorithms available in the poisson command. In particular, we are aware of three
situations in which the algorithms in the poisson command have trouble locating the
maximum and may not converge, even when the (pseudo) maximum likelihood estimates
of the Poisson regression are well de￿ned.
The simplest case in which STATA ￿nds it di¢ cult to ￿nd the Poisson (pseudo) max-
imum likelihood estimates is when y has some very large values. The following STATA
code illustrates the situation:5
drawnorm u x1 x2, n(1000) seed(101010) double clear
generate double y=exp(40+x1+x2+u)
poisson y x1 x2, robust difficult
In this example the Poisson regression does not converge, at least not in a reasonable
number of iterations. Obviously, in this case the problem can easily be by-passed just by
re-scaling the dependent variable, say, by dividing it by exp(40).
A second situation in which STATA ￿nds it di¢ cult to locate the solution of (1) occurs
when the regressors are highly collinear and have very di⁄erent magnitudes. The following
STATA code illustrates the situation:6
drawnorm u e x1, n(1000) seed(101010) double clear
generate x2=(x1<-2)
generate double x3=20+x1+(e/100)*(x1<-2)
generate double y=exp(1+x1+x2+u)
poisson y x1 x2 x3, robust difficult
In this case, again, the Poisson regression does not converge but a simple work-around
is available: if the third regressor is re-centered at zero convergence is achieved with ease.
These two examples suggest that, when facing convergence problems, researchers should
re-scale and re-center their data in a way that reduces possible numerical problems. How-
ever, even if that is done, STATA will have trouble ￿nding the (pseudo) maximum like-
5We are grateful to Alexandros Theloudis for showing us a dataset where this situation occurs.
6We are grateful to Avni Hanedar for showing us a dataset where this situation occurs.
5lihood estimates of the Poisson regression when the covariates are extremely (but not
perfectly) collinear. The following example illustrates this situation:
drawnorm u e x1, n(1000) seed(101010) double clear
generate double x2=(x1+e/20000)
generate double y=exp(1+x1+x2+u)
poisson y x1 x2, robust difficult
In cases like this it is generally not possible to bypass the problem using some sort of
data transformation and di⁄erent work-arounds are needed.7
4. WORK-AROUNDS
An obvious option to explore when the (pseudo) maximum likelihood estimates exist
but convergence is not achieved with the default options is to try one of the di⁄erent
optimization methods o⁄ered by the poisson command. However, for instance in the
third example in Section 3, none of the methods available leads to satisfactory results.
Indeed, in that case, with the NR and the BHHH options the algorithm fails to converge and
with the DFP and the BFGS options the algorithm converges to a result that is far from
the optimum. Alternatively, one can ensure convergence just by relaxing the convergence
criteria. This, however, is a risky option because the algorithm may be stopped too soon,
therefore not delivering the desired (pseudo) maximum likelihood estimates. This is what
happens, for instance, in the second example in the previous section when the nonrtol
option is used.
A simple work-around that often (but by no means always) works is to use the glm
command with the options family(poisson) link(log) irls. Indeed, the iterated re-
weighted least squares algorithm provided by the glm command appears to be much more
stable than the algorithms available in the poisson command and it produces the correct
results in the three examples presented in Section 3.
To facilitate the estimation of Poisson regressions while STATA does not improve the
reliability of poisson, we have written the ppml command which checks for the existence
of the (pseudo) maximum likelihood estimates and o⁄ers two methods to drop regressors
7Of course, the researcher may want to reconsider the speci￿cation being used.
6that may cause the non-existence of the estimates. Estimation is then implemented using
the glm method and ppml warns if the variables have large values that are likely to create
numerical problems or if there are signs that the convergence is spurious.8 Further details
on ppml can be found in the corresponding help ￿le.
5. CONCLUSIONS
In this note we have illustrated some shortcomings of the poisson command in STATA.
We believe that should be relatively easy to update the command so that it checks for the
existence of the Poisson regression estimates and is more resilient to numerical problems.
While an upgraded version of poisson is not available, practitioners can use our ppml
command, which checks for the existence of the estimates before trying to estimate a
Poisson regression, and provides several warnings about possible convergence problems.
REFERENCES
Albert, A. and Anderson, J.A. (1984). ￿On the existence of maximum likelihood esti-
mates in logistic models,￿Biometrika, 71, 1-10.
Cameron, A.C. and Trivedi, P.K. (1998). Regression analysis of count data, Cambridge,
MA: Cambridge University Press.
Gourieroux, C., Monfort, A. and Trognon, A. (1984). ￿Pseudo maximum likelihood
methods: Applications to Poisson models,￿Econometrica, 52, 701-720.
Haberman, S.J. (1973). ￿Log-linear models for frequency data: su¢ cient statistics and
likelihood equations,￿Annals of Statistics, 1, 617-632.
Manning, W.G. and Mullahy, J. (2001). ￿Estimating Log Models: To Transform or Not
to Transform?,￿Journal of Health Economics 20, 461-494.
8A tell-tale sign that the convergence is spurious is that some zero observations of y are ￿perfectly
predicted￿ ; in the second example in Section 2 the values of exp
￿
x0
i^ ￿
￿
for y = 0 vary between 3:80E￿09
and 4:20E￿09.
7Santner T.J. and Du⁄y, E.D. (1986). ￿A note on A. Albert and J.A. Anderson￿ s condi-
tions for the existence of maximum likelihood estimates in logistic regression models,￿
Biometrika, 73, 755-758.
Santos Silva, J.M.C. and Tenreyro, S. (2006), ￿The log of gravity,￿The Review of Eco-
nomics and Statistics, 88, 641-658.
Santos Silva, J.M.C. and Tenreyro, S. (2010), ￿On the existence of the maximum likeli-
hood estimates in poisson regression,￿Economics Letters, 107, 310-312.
Tobin, J. (1958). ￿Estimation of relationships for limited dependent variables,￿Econo-
metrica, 26, 24￿ 36.
Winkelmann, R. (2008). Econometric analysis of count data, 5th ed., Berlin: Springer-
Verlag.
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